{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ta\n",
      "  Downloading https://files.pythonhosted.org/packages/90/ec/e4f5aea8c7f0f55f92b52ffbafa389ea82f3a10d9cab2760e40af34c5b3f/ta-0.5.25.tar.gz\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from ta) (1.18.0)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.7/site-packages (from ta) (1.0.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/site-packages (from pandas->ta) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/site-packages (from pandas->ta) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->ta) (1.13.0)\n",
      "Building wheels for collected packages: ta\n",
      "  Building wheel for ta (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ta: filename=ta-0.5.25-cp37-none-any.whl size=24879 sha256=7f53b78a9fc5a542b536b1bd6fd928280409e9e22a683b29a49a901add30898a\n",
      "  Stored in directory: /Users/enzoampil/Library/Caches/pip/wheels/2e/93/b7/cf649194508e53cee4145ffb949e9f26877a5a8dd12db9ed5b\n",
      "Successfully built ta\n",
      "Installing collected packages: ta\n",
      "Successfully installed ta-0.5.25\n"
     ]
    }
   ],
   "source": [
    "!pip3 install ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ta import add_all_ta_features\n",
    "from ta.utils import dropna\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from tsfresh import extract_features, extract_relevant_features, select_features\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "from tsfresh.feature_extraction import ComprehensiveFCParameters\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastquant import get_crypto_data\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_crypto_data(\"BTC/USDT\", \"2019-05-13\", \"2020-08-23\").reset_index().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>dt</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>6968.24</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>6870.00</td>\n",
       "      <td>7790.71</td>\n",
       "      <td>85804.735333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>7795.62</td>\n",
       "      <td>8366.00</td>\n",
       "      <td>7599.56</td>\n",
       "      <td>7947.56</td>\n",
       "      <td>76583.722603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-05-15</td>\n",
       "      <td>7945.26</td>\n",
       "      <td>8249.00</td>\n",
       "      <td>7850.00</td>\n",
       "      <td>8169.87</td>\n",
       "      <td>37884.327211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>8169.08</td>\n",
       "      <td>8320.00</td>\n",
       "      <td>7705.00</td>\n",
       "      <td>7866.59</td>\n",
       "      <td>69630.513996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-05-17</td>\n",
       "      <td>7868.67</td>\n",
       "      <td>7925.00</td>\n",
       "      <td>6913.00</td>\n",
       "      <td>7355.26</td>\n",
       "      <td>88752.008159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>464</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>11945.10</td>\n",
       "      <td>12020.08</td>\n",
       "      <td>11561.00</td>\n",
       "      <td>11754.59</td>\n",
       "      <td>73940.169606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>465</td>\n",
       "      <td>2020-08-20</td>\n",
       "      <td>11754.38</td>\n",
       "      <td>11888.00</td>\n",
       "      <td>11668.00</td>\n",
       "      <td>11853.55</td>\n",
       "      <td>46085.254351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>466</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>11853.54</td>\n",
       "      <td>11878.00</td>\n",
       "      <td>11485.81</td>\n",
       "      <td>11531.34</td>\n",
       "      <td>64448.306142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>467</td>\n",
       "      <td>2020-08-22</td>\n",
       "      <td>11531.23</td>\n",
       "      <td>11686.00</td>\n",
       "      <td>11376.81</td>\n",
       "      <td>11662.96</td>\n",
       "      <td>43678.701646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>468</td>\n",
       "      <td>2020-08-23</td>\n",
       "      <td>11663.51</td>\n",
       "      <td>11718.07</td>\n",
       "      <td>11514.13</td>\n",
       "      <td>11648.13</td>\n",
       "      <td>37900.004690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>469 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index         dt      open      high       low     close        volume\n",
       "0        0 2019-05-13   6968.24   8100.00   6870.00   7790.71  85804.735333\n",
       "1        1 2019-05-14   7795.62   8366.00   7599.56   7947.56  76583.722603\n",
       "2        2 2019-05-15   7945.26   8249.00   7850.00   8169.87  37884.327211\n",
       "3        3 2019-05-16   8169.08   8320.00   7705.00   7866.59  69630.513996\n",
       "4        4 2019-05-17   7868.67   7925.00   6913.00   7355.26  88752.008159\n",
       "..     ...        ...       ...       ...       ...       ...           ...\n",
       "464    464 2020-08-19  11945.10  12020.08  11561.00  11754.59  73940.169606\n",
       "465    465 2020-08-20  11754.38  11888.00  11668.00  11853.55  46085.254351\n",
       "466    466 2020-08-21  11853.54  11878.00  11485.81  11531.34  64448.306142\n",
       "467    467 2020-08-22  11531.23  11686.00  11376.81  11662.96  43678.701646\n",
       "468    468 2020-08-23  11663.51  11718.07  11514.13  11648.13  37900.004690\n",
       "\n",
       "[469 rows x 7 columns]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = add_all_ta_features(\n",
    "    df, open=\"open\", high=\"high\", low=\"low\", close=\"close\", volume=\"volume\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volume_sma_em             -2.291602e+06\n",
       "volume_em                 -1.486544e+06\n",
       "momentum_wr               -4.675613e+01\n",
       "trend_dpo                 -1.208690e+01\n",
       "trend_psar_up_indicator    4.051173e-02\n",
       "                               ...     \n",
       "volatility_bbh             1.002699e+04\n",
       "volume                     5.965989e+04\n",
       "volume_fi                  5.500383e+05\n",
       "volume_obv                 5.638757e+05\n",
       "volume_adi                 1.153206e+06\n",
       "Length: 78, dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((469, 79), (469, 79))"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.iloc[: -1]\n",
    "X[\"pct_change\"] = df.close.pct_change()\n",
    "X[\"pct_change_lag1\"] = X[\"pct_change\"].shift()\n",
    "X = X.fillna(-1)\n",
    "y = df.close.pct_change().shift(-1).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "del X[\"dt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((468, 80), (468,))"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'open', 'high', 'low', 'close', 'volume', 'volume_adi',\n",
       "       'volume_obv', 'volume_cmf', 'volume_fi', 'momentum_mfi', 'volume_em',\n",
       "       'volume_sma_em', 'volume_vpt', 'volume_nvi', 'volume_vwap',\n",
       "       'volatility_atr', 'volatility_bbm', 'volatility_bbh', 'volatility_bbl',\n",
       "       'volatility_bbw', 'volatility_bbp', 'volatility_bbhi',\n",
       "       'volatility_bbli', 'volatility_kcc', 'volatility_kch', 'volatility_kcl',\n",
       "       'volatility_kcw', 'volatility_kcp', 'volatility_kchi',\n",
       "       'volatility_kcli', 'volatility_dcl', 'volatility_dch', 'trend_macd',\n",
       "       'trend_macd_signal', 'trend_macd_diff', 'trend_sma_fast',\n",
       "       'trend_sma_slow', 'trend_ema_fast', 'trend_ema_slow', 'trend_adx',\n",
       "       'trend_adx_pos', 'trend_adx_neg', 'trend_vortex_ind_pos',\n",
       "       'trend_vortex_ind_neg', 'trend_vortex_ind_diff', 'trend_trix',\n",
       "       'trend_mass_index', 'trend_cci', 'trend_dpo', 'trend_kst',\n",
       "       'trend_kst_sig', 'trend_kst_diff', 'trend_ichimoku_conv',\n",
       "       'trend_ichimoku_base', 'trend_ichimoku_a', 'trend_ichimoku_b',\n",
       "       'trend_visual_ichimoku_a', 'trend_visual_ichimoku_b', 'trend_aroon_up',\n",
       "       'trend_aroon_down', 'trend_aroon_ind', 'trend_psar_up',\n",
       "       'trend_psar_down', 'trend_psar_up_indicator',\n",
       "       'trend_psar_down_indicator', 'momentum_rsi', 'momentum_tsi',\n",
       "       'momentum_uo', 'momentum_stoch', 'momentum_stoch_signal', 'momentum_wr',\n",
       "       'momentum_ao', 'momentum_kama', 'momentum_roc', 'others_dr',\n",
       "       'others_dlr', 'others_cr', 'pct_change', 'pct_change_lag1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Train and evaluate classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a boosted decision tree on the filtered as well as the full set of extracted features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_full_train, X_full_test, y_train, y_test = X.iloc[:-50], X.iloc[-50:], y.iloc[:-50], y.iloc[-50:]\n",
    "#X_filtered_train, X_filtered_test = X_full_train[X_filtered.columns], X_full_test[X_filtered.columns]\n",
    "X_filtered_train, X_filtered_test = X_full_train, X_full_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      0.020133\n",
       " 1      0.027972\n",
       " 2     -0.037122\n",
       " 3     -0.065000\n",
       " 4     -0.013298\n",
       "          ...   \n",
       " 413   -0.005875\n",
       " 414    0.010226\n",
       " 415   -0.015756\n",
       " 416   -0.003112\n",
       " 417    0.008523\n",
       " Name: close, Length: 418, dtype: float64,\n",
       "      index     open     high      low    close        volume    volume_adi  \\\n",
       " 0        0  6968.24  8100.00  6870.00  7790.71  85804.735333  4.265263e+04   \n",
       " 1        1  7795.62  8366.00  7599.56  7947.56  76583.722603  3.561417e+04   \n",
       " 2        2  7945.26  8249.00  7850.00  8169.87  37884.327211  5.847199e+04   \n",
       " 3        3  8169.08  8320.00  7705.00  7866.59  69630.513996  2.543203e+04   \n",
       " 4        4  7868.67  7925.00  6913.00  7355.26  88752.008159  1.425209e+04   \n",
       " ..     ...      ...      ...      ...      ...           ...           ...   \n",
       " 413    413  9116.16  9238.00  9024.67  9192.56  42120.293261  2.249072e+06   \n",
       " 414    414  9192.93  9205.00  9064.89  9138.55  31463.162801  2.250691e+06   \n",
       " 415    415  9138.08  9292.00  9080.10  9232.00  38488.528699  2.267384e+06   \n",
       " 416    416  9231.99  9261.96  8940.00  9086.54  45725.168076  2.263282e+06   \n",
       " 417    417  9086.54  9125.00  9037.47  9058.26  28943.420177  2.248088e+06   \n",
       " \n",
       "        volume_obv  volume_cmf     volume_fi  ...  momentum_stoch_signal  \\\n",
       " 0    8.580474e+04   -1.000000 -1.000000e+00  ...              -1.000000   \n",
       " 1    1.623885e+05   -1.000000 -1.000000e+00  ...              -1.000000   \n",
       " 2    2.002728e+05   -1.000000 -1.000000e+00  ...              -1.000000   \n",
       " 3    1.306423e+05   -1.000000 -1.000000e+00  ...              -1.000000   \n",
       " 4    4.189026e+04   -1.000000 -1.000000e+00  ...              -1.000000   \n",
       " ..            ...         ...           ...  ...                    ...   \n",
       " 413  1.271013e+06    0.138373 -1.316667e+06  ...              28.930306   \n",
       " 414  1.239550e+06    0.129728 -1.371332e+06  ...              33.384724   \n",
       " 415  1.278038e+06    0.221278 -6.616059e+05  ...              37.455473   \n",
       " 416  1.232313e+06    0.193137 -1.517260e+06  ...              33.723689   \n",
       " 417  1.203370e+06    0.153633 -1.417440e+06  ...              30.897571   \n",
       " \n",
       "      momentum_wr  momentum_ao  momentum_kama  momentum_roc  others_dr  \\\n",
       " 0      -1.000000    -1.000000      -1.000000     -1.000000 -13.584355   \n",
       " 1      -1.000000    -1.000000      -1.000000     -1.000000   2.013295   \n",
       " 2      -1.000000    -1.000000      -1.000000     -1.000000   2.797211   \n",
       " 3      -1.000000    -1.000000      -1.000000     -1.000000  -3.712177   \n",
       " 4      -1.000000    -1.000000      -1.000000     -1.000000  -6.500021   \n",
       " ..           ...          ...            ...           ...        ...   \n",
       " 413   -62.031679  -338.369441    9352.992996     -2.879831   0.835971   \n",
       " 414   -67.734952  -349.778265    9347.375709     -2.639693  -0.587540   \n",
       " 415   -57.866948  -341.569588    9346.407205     -0.840259   1.022591   \n",
       " 416   -73.227033  -313.513765    9309.077553     -2.910690  -1.575607   \n",
       " 417   -76.213305  -297.808647    9274.625812     -2.543710  -0.311230   \n",
       " \n",
       "      others_dlr  others_cr  pct_change  pct_change_lag1  \n",
       " 0     -1.000000   0.000000   -1.000000        -1.000000  \n",
       " 1      1.993297   2.013295    0.020133        -1.000000  \n",
       " 2      2.758803   4.866822    0.027972         0.020133  \n",
       " 3     -3.782832   0.973981   -0.037122         0.027972  \n",
       " 4     -6.720897  -5.589349   -0.065000        -0.037122  \n",
       " ..          ...        ...         ...              ...  \n",
       " 413    0.832496  17.993867    0.008360         0.011579  \n",
       " 414   -0.589273  17.300605   -0.005875         0.008360  \n",
       " 415    1.017398  18.500111    0.010226        -0.005875  \n",
       " 416   -1.588151  16.633015   -0.015756         0.010226  \n",
       " 417   -0.311715  16.270019   -0.003112        -0.015756  \n",
       " \n",
       " [418 rows x 80 columns])"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, X_full_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12448fed0>"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#regressor_full = RandomForestRegressor()\n",
    "regressor_full = LinearRegression()\n",
    "regressor_full.fit(X_full_train, y_train)\n",
    "#print(classification_report(y_test, classifier_full.predict(X_full_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121156dd0>"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4ciy7qt7oK+Md05pmQmuKf7pkMY//1yX87y+eyWNXnsNn33ciN160iNac92hJkdc1N/caC0ecr7x3G9d8YmFJy8WKSOkaotdTo9u65wApy78BD9/sv3bugrzXw56y+0NGvd20YfuRmkCY3Bx3CuPNIj2UclM7c6ZOxFIGEbWKtrTxw8+dntflNrcMw+mSrXsOMLxSXlgZi6WZTp09hceuPEfpFpEKUo2ixt2zaTdfuq07dD6nmzZsz6tVFHvK/uuz5o343rZ0Om9uqTBLF8/msSvP4YefO532lvBflcODQ0dSO8PBZUJrimPb06QtO1ZjWEsKvnvxaZx98vRRpxI/++S3RJ4X1W7TMamd0+YeryAhUiGqUdSw/PaFkYZv9qP1jnr/2zv450d3khtrSs3dZ2/a07nxomzPqb6CwXmFndDCRpyPVjsYD/XqEakeBYoaNtrKcYU3+2JP2bv+/RDO0QFyuZMGlmrp4tkcf0wbX759Y14aamJrS+j6FLn7w91tixnvADn16hGpDgWKGlasQfnY9vSRpUqLLSCU20ax8t5tebPC5k4aOBanzJpMhsKur+X1Krpn026uWLOZtKUY8syRiQpLpV49IvFToKhhoTf+Tyxk7gkTGU7lFCp8yg6fwG98Ywsqne7pPdjH3961KVgkKVtL+cZdm5pygJxILVOgqHGFN/5Hd7zKits3Rk5RUfiUXcmxBZVM92zd8xqF8xEOZrKvF5tjSkSqT72e6sBwLx5g1NXqwr53uCdSpcYWVK5XUbFutA07S4tIXVKNoo6Md4qKWm30PWXWFFrTltd+0po2Tpk1JcFSiUgh1SjqSKPNCNoxqZ3vXXwa7S0pjmlL096S4nsXh4/CrtY62iIykmoUdWS8jcmVmnq7WDfWctZ/GK22E1b2WqwdiTQya7RZu7u6ury7uzvpYsRqLDfm3oN9R5ZYHTahNcVjV54zpptssWAT53KbYWVvSWW79+bOWjvez4t7PWmtVy31xMw2untX2DHVKOrQWMYOVGLq7WLrPCycOTnW9R/Cyj6YgcFM5sgI8fF+XtzrSWu9amkkaqNocJVo1wibgbY1lWJTsM534eujzSFVqmIDDsv9vLA5sUbrPVZL7y9SbQoUDa4S3WOLBZvFc4+PtXG9sOztLZY3Bfl4P69Y4KtUgIv7/UWqTamnEI2WWy63e2yxRvT5M46LfWK+wrI/tuPVsj8v7t5jjdY7TUSN2QWUWy4ujl5PlSzHWKzdtHtEwKnkzznu9xeptKjGbAWKHJXqIST1Qb2eRI5Sr6cSVaKHkNSPuGee1cy20ijUmJ1DuWURkZEUKHLEMYFeHDSlhYhUU2TqycxeJ3wqTwPc3UcuiFDnanUCvWFqbBeRaosMFO5+XLUKUktqNbdcbIS0FvoRkTiNqTHbzN4CTBjed/cXKl4iKSqssT2dMjW2i0isSmqjMLOlZrYd+CPwEPA88IvxfqiZnWBm95vZ9uDfqUXO+6WZ7Teze8f7WY0krLH9jb4hntl9IKESiUgzKLUxeyXwPuA5dz8JOBd4oozPvQpY7+4LgPXBfpgbgc+V8TkNpWNSO9dcsHDE6yvv26aGbRGJTamBYsDde4GUmaXcfQMQOjCjRMuA1cH2auCTYSe5+3rg9TI+p+GcOmsKx7al817TPEIiEqdS2yj2m9kk4GHgDjN7GXijjM+d4e57g+0XgRllvBdmtgJYAdDZ2VnOW9W8OVMnMlQwml5jPUQkTqXWKJYBh4D/AvwS+APwF1HfYGYPmNkzIV/Lcs/z7BwiZc0j4u6r3L3L3bumT59ezlvVvHoZ6yEijaOkGoW759YeVhc9Mf97lhQ7ZmYvmdlMd99rZjOBl0t5T8mq9bEeItJYSu319LqZvRZ8HTazITN7rYzPXQssD7aXA/eU8V5NqWNSO6fNPV5BQkRiV1KgcPfj3H1yMBJ7InAh8IMyPvc64Lygy+2SYB8z6zKzW4dPMrNHgJ8B55pZj5n9eRmfKSIi4zDuacbN7Lfu/u4Kl6ds5a5HISLSjMqeZtzM/lPObops19jDFSibiIjUuFK7x+b2cBokOzJ7WfipIiLSSEoNFLe6+2O5L5jZWai3kohIwyt1HMX/KvE1ERFpMKOtR/F+4APAdDP7Rs6hyUA6/LtERKSRjJZ6agMmBeflrk3xGnBRXIUSEZHaMdrCRQ8BD5nZv7r7n6pUJhERqSGltlHcambHD++Y2VQz+1VMZRIRkRpSaqCY5u77h3fcfR/wlniKJCIitaTUQJExsyPzd5vZPMqc8VVEROpDqeMorgYeNbOHAAM+RLD+g4iINLZSpxn/pZl1kQ0OvwV+TnZ9ChERaXClzvX0ReDrwBxgE9n1sx8HzomvaCIiUgtKbaP4OvBe4E/u/lHg3cD+6G8REZFGUGqgOOzuhwHMrN3dfw+8I75iiYhIrSi1MbsnGEfxc+B+M9sHaACeiEgTKLUx+z8Gm98xsw3AFOCXsZVKRERqRqk1iiOCaT1ERKRJlNpGISIiTUqBQkREIilQiIhIJAUKERGJpEAhIiKRFChERCSSAoWIiERKJFCY2Qlmdr+ZbQ/+nRpyzmIze9zMtprZFjP7VBJlFRFpdknVKK4C1rv7AmB9sF/oTeAv3f0U4Hzgf+QuxyoiItWRVKBYBqwOtlcDnyw8wd2fc/ftwfYe4GVgetVKKCIiQHKBYoa77w22XwRmRJ1sZmcAbcAf4i6YiIjkG/NcT6UysweAt4Ycujp3x93dzIquv21mM4HbgeXunilyzgqCpVk7OzvDThERkXGKLVC4+5Jix8zsJTOb6e57g0DwcpHzJgP3AVe7+xMRn7UKWAXQ1dVVNOiIiMjYJZV6WgssD7aXA/cUnmBmbcC/Abe5+5oqlk1ERHIkFSiuA84zs+3AkmAfM+sys1uDcy4BzgY+b2abgq/FyRRXRKR5mXtjZWq6urq8u7s76WKIiNQVM9vo7l1hxzQyW0REIilQiIhIJAUKERGJpEAhIiKRFChERCSSAoWIiERSoBARkUgKFCIiEkmBQkREIilQiIhIJAUKERGJpEAhIiKRFChERCSSAoWIiERSoBARkUgKFCIiEkmBQkREIilQiIhIJAUKERGJpEAhIiKRFChERCSSAoWIiERSoBARkUgKFCIiEkmBQkREIiUSKMzsBDO738y2B/9ODTnnRDP7jZltMrOtZvblJMoqItLskqpRXAWsd/cFwPpgv9Be4P3uvhg4E7jKzGZVsYwiIkJygWIZsDrYXg18svAEd+93975gtx2lyUREEpHUzXeGu+8Ntl8EZoSdZGZzzWwLsAu43t33VKuAIiKS1RLXG5vZA8BbQw5dnbvj7m5mHvYe7r4LWBSknH5uZmvc/aWQz1oBrADo7Owsu+wiInJUbIHC3ZcUO2ZmL5nZTHffa2YzgZdHea89ZvYM8CFgTcjxVcAqgK6urtCgIyIi45NU6mktsDzYXg7cU3iCmc0xs4nB9lTgg8CzVSuhiIgAyQWK64DzzGw7sCTYx8y6zOzW4Jx3Ak+a2WbgIeC77v50IqUVEWlisaWeorh7L3BuyOvdwBeD7fuBRVUumoiIFFCXUxERiaRAISIikRQoREQkkgKFiIhEUqAQEZFIChQiIhJJgUJERCIpUIiISCQFChERiaRAISIikRQoREQkkgKFiIhEUqAQEZFIChQiIhJJgUJERCIpUIiISCQFChERiaRAISIikRQoREQkkgKFiIhEUqAQEZFIChQiIhJJgUJERCIpUIiISCQFCgGg92Afm3ftp/dgX9JFEZEa05LEh5rZCcBPgXnA88Al7r6vyLmTgW3Az9398mqVsZncs2k3V969hdZUioFMhhsuXMTSxbOTLpaI1IikahRXAevdfQGwPtgvZiXwcFVK1YR6D/Zx5d1bODyQ4fW+QQ4PZLji7i2qWYjIEUkFimXA6mB7NfDJsJPM7HRgBvB/q1SuptOz7xCtqfxfg9ZUip59hxIqkYjUmqQCxQx33xtsv0g2GOQxsxTwPeCb1SxYs5kzdSIDmUzeawOZDHOmTkyoRCJSa2ILFGb2gJk9E/K1LPc8d3fAQ97iK8A6d+8p4bNWmFm3mXW/8sorFbqC5tAxqZ0bLlzEhNYUx7W3MKE1xQ0XLqJjUnvSRRORGmHZ+3SVP9TsWeAj7r7XzGYCD7r7OwrOuQP4EJABJgFtwA/cPao9g66uLu/u7o6p5I2r92AfPfsOMWfqRAUJkSZkZhvdvSvsWCK9noC1wHLguuDfewpPcPfPDm+b2eeBrtGChIxfx6R2BQgRCZVUG8V1wHlmth1YEuxjZl1mdmtCZRIRkRCJpJ7ipNSTiMjYRaWeNDJbREQiKVCIiEgkBQoREYmkQCEiIpEUKEREJJIChYiIRFKgEBGRSA03jsLMXgH+FHHKNODVKhWnFuh6G1+zXbOuNx4nuvv0sAMNFyhGY2bdxQaVNCJdb+NrtmvW9VafUk8iIhJJgUJERCI1Y6BYlXQBqkzX2/ia7Zp1vVXWdG0UIiIyNs1YoxARkTFo+EBhZieY2f1mtj34d2rIOYvN7HEz22pmW8zsU0mUtRxmdr6ZPWtmO8xsxAJPZtZuZj8Njj9pZvOqX8rKKeF6v2Fm24Kf53ozOzGJclbKaNebc96FZuZmVve9gkq5ZjO7JPg5bzWzn1S7jJVUwu90p5ltMLPfBr/XH69a4dy9ob+AG4Crgu2rgOtDzjkZWBBszwL2AscnXfYxXGMa+APwNrJLxm4GFhac8xXgh8H2pcBPky53zNf7UeCYYPtvGv16g/OOAx4GniC7ImTiZY/5Z7wA+C0wNdh/S9Lljvl6VwF/E2wvBJ6vVvkavkYBLANWB9urgU8WnuDuz7n79mB7D/AyEDrwpEadAexw953u3g/cSfa6c+X+f1gDnGtmVsUyVtKo1+vuG9z9zWD3CWBOlctYSaX8fAFWAtcDh6tZuJiUcs1fAm52930A7v5ylctYSaVcrwOTg+0pwJ5qFa4ZAsUMd98bbL8IzIg62czOIBvR/xB3wSpoNrArZ78neC30HHcfBA4AHVUpXeWVcr25vgD8ItYSxWvU6zWz9wBz3f2+ahYsRqX8jE8GTjazx8zsCTM7v2qlq7xSrvc7wGVm1gOsA75WnaJBS7U+KE5m9gDw1pBDV+fuuLubWdFuXmY2E7gdWO7umcqWUpJgZpcBXcCHky5LXMwsBfwj8PmEi1JtLWTTTx8hW2N82Mze5e77Ey1VfD4N/Ku7f8/M3g/cbmanVuNe1RCBwt2XFDtmZi+Z2Ux33xsEgtDqqZlNBu4Drnb3J2Iqalx2A3Nz9ucEr4Wd02NmLWSrrr3VKV7FlXK9mNkSsg8LH3b3viqVLQ6jXe9xwKnAg0E28a3AWjNb6u71uoB8KT/jHuBJdx8A/mhmz5ENHE9Vp4gVVcr1fgE4H8DdHzezCWTngYo95dYMqae1wPJgezlwT+EJZtYG/Btwm7uvqWLZKuUpYIGZnRRcy6VkrztX7v+Hi4Bfe9AqVodGvV4zezfwI2BpneeuYZTrdfcD7j7N3ee5+zyybTL1HCSgtN/pn5OtTWBm08imonZWs5AVVMr1vgCcC2Bm7wQmAK9UpXRJt/bH/UU2D78e2A48AJwQvN4F3BpsXwYMAJtyvhYnXfYxXufHgefItq1cHbx2LdkbBsEv1c+AHcD/A96WdJljvt4HgJdyfp5rky5znNdbcO6D1HmvpxJ/xkY25bYNeBq4NOkyx3y9C4HHyPaI2gT8h2qVTSOzRUQkUjOknkREpAwKFCIiEkmBQkREIilQiIhIJAUKERGJpEAhUmPM7GDSZRDJpUAhUgVmlk66DCLjpUAhUiYzm2dmvzezO8zsd2a2xsyOMbPnzex6M/sNcLGZvd3MfmlmG83sETP7s+D7TwrWQ3nazP4h4csRGUGBQqQy3gH8wN3fCbxGdv0PgF53f4+730l2PYGvufvpwDeBHwTnfB+4xd3fRXYtFJGaopHZImUKVgt82N07g/1zgP8MLCY7IeGfzGwS2Xl5ns351nZ3f6eZ9QJvdfeBYHLKPe4+qaoXIRKhIWaPFakBhU9cw/tvBP+mgP3uvrjE7xepGUo9iVRGZ7BGAMBngEdzD7r7a55jauYAAACCSURBVGSnwr4YwLJOCw4/Rna2UIDPVqOwImOhQCFSGc8CXzWz3wFTgVtCzvks8AUz2wxs5ehSl18PvvdpolfqE0mE2ihEyhS0Udzr7qcmXBSRWKhGISIikVSjEBGRSKpRiIhIJAUKERGJpEAhIiKRFChERCSSAoWIiERSoBARkUj/H8/9gheFV0O4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of sample\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "pdf = pd.DataFrame(dict(pred=regressor_full.predict(X_full_test), actual=y_test))\n",
    "\n",
    "pdf.plot.scatter(0, 1)\n",
    "#plt.xlim(-0.025, 0.025)\n",
    "#plt.ylim(-0.15, 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pred</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.332945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>0.332945</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pred    actual\n",
       "pred    1.000000  0.332945\n",
       "actual  0.332945  1.000000"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.1)"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In sample\n",
    "# The in sample predictions from linear regression are not too overfit, compared to random forest - looks promising\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "pdf = pd.DataFrame(dict(pred=regressor_full.predict(X_full_train), actual=y_train))\n",
    "\n",
    "pdf.plot.scatter(0, 1)\n",
    "plt.xlim(-0.1, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearRegression' object has no attribute 'feature_importances_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-398-fe84e39d411f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeat_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"importance\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mregressor_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"feat\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX_full_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'LinearRegression' object has no attribute 'feature_importances_'"
     ]
    }
   ],
   "source": [
    "feat_importance = pd.DataFrame({\"importance\": regressor_full.feature_importances_, \"feat\": X_full_train.columns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010040</td>\n",
       "      <td>index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011414</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022711</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015858</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022280</td>\n",
       "      <td>close</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.008654</td>\n",
       "      <td>others_dr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.017034</td>\n",
       "      <td>others_dlr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.021043</td>\n",
       "      <td>others_cr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.008210</td>\n",
       "      <td>pct_change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.022445</td>\n",
       "      <td>pct_change_lag1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    importance             feat\n",
       "0     0.010040            index\n",
       "1     0.011414             open\n",
       "2     0.022711             high\n",
       "3     0.015858              low\n",
       "4     0.022280            close\n",
       "..         ...              ...\n",
       "75    0.008654        others_dr\n",
       "76    0.017034       others_dlr\n",
       "77    0.021043        others_cr\n",
       "78    0.008210       pct_change\n",
       "79    0.022445  pct_change_lag1\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trend_dpo</th>\n",
       "      <td>0.039516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume_adi</th>\n",
       "      <td>0.034599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend_cci</th>\n",
       "      <td>0.030592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume_obv</th>\n",
       "      <td>0.029656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend_visual_ichimoku_a</th>\n",
       "      <td>0.028776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatility_bbli</th>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend_psar_down_indicator</th>\n",
       "      <td>0.000392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatility_kchi</th>\n",
       "      <td>0.000119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatility_kcli</th>\n",
       "      <td>0.000078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatility_bbhi</th>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           importance\n",
       "feat                                 \n",
       "trend_dpo                    0.039516\n",
       "volume_adi                   0.034599\n",
       "trend_cci                    0.030592\n",
       "volume_obv                   0.029656\n",
       "trend_visual_ichimoku_a      0.028776\n",
       "...                               ...\n",
       "volatility_bbli              0.000416\n",
       "trend_psar_down_indicator    0.000392\n",
       "volatility_kchi              0.000119\n",
       "volatility_kcli              0.000078\n",
       "volatility_bbhi              0.000066\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importance.set_index(\"feat\").sort_values(ascending=False, by=\"importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TA Feature Importance for Predicting BTC returns')"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NU3Upaeqln1f6h6M0P3HfRsteCtYGvl9MyGMFDyH1Yv2lsOoq0ntnuBYNx7w+adrFIN0c14g3gOUl9a6w7rn8d1BZ/do69w2JiDGkBt5Okr5dvl5pXt4Vc96HSe+dwyQdWak8Sf0kfbTSugbcROr5PFrS9mXrvsPC4V0dHl7Q2dr5Wbgq/z1bUs9CvlVp7DzfTOoBP1TSYp9jSR8rS3qDNAxisfnUO9H9pLG5+2rxedK/SfvGX1eUe/u/kZ8+0MHifkIag/8FSb9V5fgEPfOUqyd3cF+QxtpPAo6UdFilDJK2VwpuU9Le7/kBwIkdqGvdJG2jCvNws/DKRUenFFxiPETErAG5UVQMoVtRRDwl6WFSr8vnWfxGqnaLiL/mm1iGAv8r6Q7SneVrkv7Z7E66MaT0y7409d/3c2NjWs63P6nxN6TCbkaRpwmT1J906fjDWBj57LekKbLOzV+2T5FmvPg8aQq6hhvZwA9Jd5F/CzhY0v2ksYzrkuaaHkgaG9ls0eEeAL4laRfSlYuPsXA2jBPKboD8CbAPqfH9aUl3keZBPpTUUD8vIuqdR7jkPtJ8wn9RCurxPulGyutp37lvj8NIDaGfKwUueYj0Q2JT0ntiIxYOURiS63xtbpCPJ10N+jhpOMvmpBvb3qCdIuItSV8njTkfI+lG0tWDHUmXl/9BmrWlWTX0WYiIUZJ+S2oIzZR0E+ny/sGkBnNdr2VEvC/pENK9IzdLGkU6P6sAW5B6/lcrbHIf6Xvgzvx9NxeYGBGddpNkvpHx66T5ku/M5/I50nvkc6Tv1n1YOJyhXntpYQCk5UjDhr5Iutnxb3Rw2EFE/EvS3qTP4NeBwZLuJs8CQjqPe5DGRB/TkX3l/UU+d/cBf5T0/0jn7m3Sd9J2pO/orVnYMz2adM6+l388vZbTf5bvUxkBnA0cn6+YTgI2Js2rfSvpe2tJO4D0w3EM8D+kTouNSPc5zSNNZdmcunoaEz/8aLYHbUzTx8JppMbVUc7ROe/NdeQtTdN3ZAP1/Cypx+kl0pfkq6QvwJ8C25Xl7UOaWuqfpBlIppMCPaxKham08jbHkhpk7+c875et35b0z2026Ut8FOkfcFvT9FWc0quQZznSP5v7Sf8E5pIaQw+RbjrasM7Xpq1p+qpNL1hxyre8brFpC1k4hd7lpH9aI0k/RN7N9d2jyn5WJf3Tejy/tm/n/ItNlVXcRxvHugLwn/l9+0H5+Wz03Fc61nrqQ2r8/ZTUOJmTz98k0pR1K5Xl7Zlfg8n5/fMuKYLeX0iNkcXmhm70nOX1A0mRFN/I76XnSdOwVTrHbZZVox4V58FuI3+nfxZy/m+TfuzOyXkvIs1Istj+qPI5zes2JgWreT7v93XSj8czy/KtSQpY8k/SDWoL3hvUnqav0ffXrqTvmHdIVyfuJg1TuDJv07et17PC/ssfs/P7cSiFOfJrvO8qTtNXlrcHcDgpoM6L+dy8S/r8Xw7sUGe9S9P0XVIjXy/SzbFT8mv1Lilozm2kma9WLst/IKmn+p3Ca7F2Yf0nST8SSt9tj5JuNGxrmr7ZbdSv6tSApB8bAYwspG2T38eT8vvwfdKPlOuBAY1+VpfmQ/kAzMysAfkS/RNUuIHQzJYOSRNJjc81ImJuV9fHrMRjsM3MzKxpSVoth8wuTz+JNAf47W5cW7PxGGwzMzNrZpsCj+QxzE8DKwL9SUOA3iDduGrWVNzANjMzs2b2Imnc8+6k0Okrku49uRK4ICKe67qqmVXmMdhmZmZmZp3IPdjWNNZee+3o06dPV1fDzMzMrKaJEye+HhHrVFrnBrY1jT59+jBhQqNTAJuZmZktfZKer7bOs4iYmZmZmXUi92B3E5J6AYdHxKVLqPzZEbF6nXmvJk0Ef1Nn1mH6i7Poc9btnVmkmZmZLUOeG7ZfV1cBcA92d9ILOLk8UZJ/JJmZmZk1ETfOuo9hwCaSppDCIb9PCp/bF9hM0pHAt0jTFz0GnBwR8yTNJoUZ3Z8UJvnAiHhF0sbAH0mhSf/c1o4lCfgVsDfwd1Lo3NK654D/JoUQf4/Uy/6/kvoAvwPWBl4Djo2IFyqUfQJwAkCPNSveJ2BmZmbWrbgHu/s4C3g6IvoBZ5KiV50WEZtJ2hwYAuyS188DjsjbrQaMjYhtgYeA43P6RcBlEbE1aT7RthwMfBrYAjga2Lls/axcziXAL3Par4BrImIb4Drg4koFR8QVETEgIgb0WLVnzRfBzMzMrNm5gd19jYuIZ/PynqSoVuNzD/eewCfzurnAyLw8EeiTl3cBrs/L19bY1+7A9RExLyL+CYwqW3994e/AvDyQ1ENeKn/XOo7JzMzMrNvzEJHu653Cski9xd+rkO+DWBhNaB6LnvPOijIUVZYbsvWGPZnQJDcnmJmZmbWXe7C7j7eBNaqsuw8YLGldAElrSdqoRnljgK/k5SPaykgaWjJEUg9JvYHPla0fUvj7aF5+pKz80TX2YWZmZtYS3IPdTUTEG5LGSJpBupnwlcK6xyX9ELhb0nKkmyBPAapOgA6cBvxR0nepcZMjcCuwB/A48AILG9ElH5E0DZgDHJbTTgWuknQm+SbHOg7TzMzMrNvTwtEDZo3Ls4gMiIjXO1rWgAEDwpEczczMrDuQNDEiBlRa5yEiZmZmZmadyENEuqk8z/TIiNiqE8vcmsVnFJkTETtV2yYi+lQpaygwOyJ+Kuk84KGIuLet/TuSo5mZWfs0SwRDS9zAtgUiYjrQbwmUe3Znl2lmZmbWrDxEpIlIGibplMLzoZLOlDRc0gxJ0yUNqbDdMZIuKTwfKWlQXp6dt58p6V5JO0p6QNIzkg7IeXrkPOMlTZN0Yht1XF3SfZIm5focWFj3A0l/k/QwKTBNKf1qSYM7+vqYmZmZdQduYDeXEcChheeHAq+SepW3BfYChuep8uq1GjAqIrYkTfV3Pink+cHAeTnPcaRojDsAOwDH51DqlbwPHBwR25Om6/uZkv6kafn6AV/M5dQk6QRJEyRNmPfurAYOy8zMzKw5eYhIE4mIyZLWlbQBsA7wJqnBen1EzANekfQgqfE6rc5i5wJ35uXppDHVH0iazsKojp8Htin0MvcENgWeZXEC/kPS7sB8YENgPWA34NaIeBdA0m11HvMVwBUAK/Xe1FPamJmZWbfnBnbzuREYDKxP6tGu1pNc9CGLXo1YubBcjOQ4nzRXNRExX1Lp/As4NSLuqmNfR5Aa//1zQ/25sv21myM5mpmZWSvwEJHmM4I01GIwqbE9moVRFNcBdgfGlW3zHNBP0nKSPg7s2OA+7wK+IWkFAEmbSVqtSt6ewKu5cf05oBQx8iHgIEmrSFoD+FKDdTAzMzNrCe7BbjIRMTM3UF+MiJck3QoMBKYCAXwnIl7O0/SVjCEN53gceAKY1OBuryQNF5kkSaTIiwdVyXsd8Jc8xGQC8GSu9yRJI3I9XwXGN1gHMzMzs5bgSI7WNBzJ0czMzLoLR3I0MzMzM1tKPETEKmpPVMeOciRHMzNb0hzx0JYGN7C7KUm9gMMj4tIltItHI2L1jhaSpxy8OCIcaMbMzMyWCR4i0n31Ak4uTyxMvdcUIuKfblybmZnZssQN7O5rGLCJpCk5xPnoHNzlcQBJR0oal9f/RlKPnD5b0gWSpkoaK2m9nL6xpEdz+PPza+1c0ndz3qmShuW0T+Vw7FNzKPVNJPWRNKONchzJ0czMzFqKG9jd11nA0xHRDzgT2B44LSI2k7Q5MATYJa+fRwoQAyl0+tiI2JY0d/XxOf0i4LKI2Bp4qa0dS9oXOBDYKZfzn3nVdcCvc9rOtcqBFMkxIgZExIAeq/as99jNzMzMmlZTDSewDhkXEaXQ5nsC/YHxaVprViHNTQ0pdPrIvDwR2Dsv7wJ8OS9fC1zYxr72Aq4qhUWPiP/Lc3dvGBG35rT3AfL+6+JIjmZmZtYK3MBuHe8UlgVcExHfq5CvGDp9Hou+BzwpupmZmVkHeYhI9/U2sEaVdfcBgyWtCyBpLUkbVclbMoYUoh0WDiep5h7gWEmrlsqPiLeBf0g6KKetVFpvZmZmtixxA7ubiog3gDH5BsLhZeseB34I3C1pGqlB3LtGkacBp+QQ6BvW2PedwG3ABElTgDPyqqOAb+V9PgKs39hRmZmZmXV/DpVuTcOh0s3MzKy7cKh0MzMzM7OlxDc5diOS+gAjI2KrpbS/NsOlSxoEnBER+3fG/hwq3czMGuXQ59aM3MC2qiJiOtCvq+thZmZm1p14iEgXkzRM0imF50MlnSlpuKQZOVrikArbHSPpksLzkblHuRStcbikmTmy4o6SHpD0jKQDcp4eOc94SdMkndhGHdVGfdaUdLukpyRdLmk5SSdJGl7YfpG6lpXtSI5mZmbWUtzA7nojgEMLzw8lBYXpB2xLCuoyXFKtWUCKVgNGRcSWpOn8zicFlDkYOC/nOQ6YFRE7ADsAx0vauEp5/9ZGfXYETgW2ADbJeW/O+yoZAtxQqWBHcjQzM7NW4wZ2F4uIycC6kjaQtC3wJqkxe31EzIuIV4AHSY3ges0F7szL04EHI+KDvNwnp38eODpPs/cY8FFg0yrl7dpGfcZFxDMRMQ+4Htg1Il4DnpH0GUkfBfqS5tk2MzMza3keg90cbgQGk+aNHgFU60ku+pBFfyCtXFguRmucD8wBiIj5kkrnXMCpEXFXRyrO4tEfS89vIPXGPwncGnXMB+lQ6WZmZtYK3IPdHEaQoigOJjW2RwND8jjpdYDdgXFl2zwH9Mtjnj9OGqrRiLuAb0haAUDSZpJWq5K3rfrsKGljScuRhoI8nNNvBQ4EDqPK8BAzMzOzVuQe7CYQETMlrQG8GBEvSboVGAhMJfUIfyciXs7T9JWMAZ4FHgeeACY1uNsrScNFJkkS8BpwUJW81erTFxgPXAJ8Crg/5yUi3pT0BLBFRJT/ODAzMzNrWY7kaE3DkRzNzMysu3AkRzMzMzOzpaQphohI6gUcHhGXLqHyZ0fE6g1uMwA4OiK+1Yn1uJoUifGmKuuvBH4eEY9XWT8UmB0RP62w7pGI2LmD9duaNEPIrPyAQuTGJc2RHM3MzJEZrRU0RQMb6AWcDCzSwJa0fER82BUViogJwFIdrxARX+/Ath1qXOcypku6jTZ+BJiZmZlZ25pliMgwYBNJU3JkwdG5ofc4gKQjJY3L638jqUdOny3pAklTJY2VtF5O31jSoznq4Plt7VjSDZL2Kzy/WtJgSYMkjcxpn837niJpsqQ1iutznkskHZOXz87HMUPSFfkmwppytMUBefkLkiblY7uvkG2LQlTGbxW2nZ3/DpL0oKQ/5zzDJB2RX7/pkjbJ+fpIGpWjON4n6RMV6vPj/Hr0kPScpLVz+gBJD7RxHDvm13+ypEckfbqNvI7kaGZmZi2lWRrYZwFPR0Q/4Exge+C0iNhM0uak6d92yevnAUfk7VYDxkbEtsBDwPE5/SLgsojYGnipxr4XRFKUtCKwJ1A+TuEM4JS8/92A92qUeUlE7BARWwGrAPvXyL+IPBXeb4Ev52M7pLC6L7APaVq+c0rT7JXZFjgJ2Bw4CtgsInYkzRxyas7zK+CaiNgGuA64uKwOw4F1gGNzEJlGPAnsFhHbAWcD/1EtoyM5mpmZWatplgZ2uXER8Wxe3hPoD4zPUQf3BD6Z180FSr3IE1kYpXAXUlRBgGtr7OsO4HOSVgL2BR6KiPIG9Bjg57nHuFcdw1Y+J+kxSdOBPYAta+Qv95lcj2cBIuL/Cutuj4g5EfE6KaT6ehW2Hx8RL0XEHOBp4O6cXozkOBD4Y16+lhStseRHQM+IOKmeADEV9ARulDQD+AWNH7+ZmZlZt9UsY7DLvVNYFqmn9XsV8hUjFs5j0eOpq2EYEe/n4Q77kHrKFwuKEhHDJN0OfBEYI2kfqkRSlLQyaSz5gIj4e74xcWU6z5zCcvkxV8ozv/B8fpX85cYD/SWtVWjcF4+31vH8GLg/Ig7Oc3c/UMc+HcnRzMzMWkKz9GC/DaxRZd19wGBJ6wJIWkvSRjXKG0OKjAgLh5O0ZQRwLGn4x53lKyVtEhHTI+JCUuOzL/A8aTz0SnkWlD1z9lLj83VJq5OiMzZqLLC7pI3z/tdqRxm1PMKir5xywQsAACAASURBVNHowro7SePib88BcCBFjuyfl79co+yewIt5+ZiOVtTMzMysO2mKBnZEvEHqGZ4BDC9b9zjwQ+BuSdOAe4DeNYo8DTglD9HYsI4q3A18Frg3IuZWWH96vmFxGvABcEdE/B34b2BG/js51/ct0vjpGaRw5OPr2P8iIuI14ATgFklTST8AOtupwLH5mI4ivWbFOtxIOo7bJK0CnAtcJGkCqee8Lf8J/ETSZJr3KomZmZnZEuFIjtY0HMnRzMzMugs5kuOyQVI/SV/s6nqYmZmZLcuWmcv3OUph+YwiSy1KYVldbgU2Lkv+bkTc1cGi+wEDgL92sJy6SDqOhdP+lYyJiFPaU54jOZqZtTZHabRlxTLTg51vUuxX9mh34zoHankyB2L5m6TrJO0laYyk/8nBVtaS9KcczGWspG3y5lPz423gI8B5wJ45EMydpbmtJfXPQWMmSrpLUu+c/oCkC3PwmL9J2i3P4X0eMEQpIM4QSUMlnVGo84xc75p1b+O4h0q6VtIYYC/SlIKTgR6kmVtuyvl6SPppaey6pPKGuJmZmVlLWmYa2EvIp4CfkWYV6QscTppP+gzg+6QbAyfnYC7fB35f2HYT0hzZBwB/IE1rtzUpiM1+uZH9K2BwRPQHfgdcUNh++Rw85nTgnHxz5tnAiPzjodaNkbXq3pYtgL0i4jDgFCBy3Q8DrslTFZ5AmnO7XyGYzWIcydHMzMxazTIzRGQJeTYipgNImgncFxGRZy/pA2xEntIuIkZJ+qikNfO2d0TEBzlvDxZOD1ja9tPAVsA9SpHWe7BoVMpb8t9igJ3OrHtbbisE49mV9EOAiHhS0vPAZqTe7ctLQXnKguUsEBFXAFcArNR7U99xa2ZmZt2eG9gdUyugywe1to2I+ZKKAXNK2wqYGREDa+y7WrAZqBIMp866t+WdGuvNzMzMllluYC9Zo0lBXH4saRDwekT8K/dI1/IUsI6kgRHxaB4ysllEzGxjm/KAPc8B+wNI2p7Fb6zsDKVjHCVpM+ATue73ACdKuj8iPiyLClmRIzmamZlZK/AY7CVrKCnk+DRSZMSv1rthHlM9GLgwB5uZAuxcY7P7SdElp0gaAtwMrJWHgHwT+Fvjh1DTpcByeWjJCOCYiJgDXAm8AEzL9T98CezbzMzMrOk40Iw1DQeaMTMzs+7CgWbMzMzMzJYSN7ANWDCv94zC82PzUJPi49ddWUczMzOz7sA3OVpFEXEVcNXS3KcjOZqZVecoiGbdh3uwW5ikYZJOKTwfKulMScNzhMXp+WbI8u2OkXRJ4fnIPAsKkmbn7WdKujdHrHxA0jOSDsh5euQ843MUxxOXwuGamZmZNQU3sFvbCODQwvNDgVeBfsC2pGAww0sh2Ou0GjAqIrYkTQt4PrA3cDApVDvAccCsiNgB2AE4XtKSmCLQzMzMrOl4iEgLi4jJktaVtAGwDvAmqXF9fUTMA16R9CCpETytzmLnsmjUyTmFiJR9cvrngW0kDc7PewKbAs+WFybpBFJYdXqsuU6DR2hmZmbWfNzAbn03kubTXp/Uo11PT3JbESDLo04WI1KW3k8CTo2Iu2rtyKHSzczMrNW4gd36RgC/BdYGPgsMJEVYvAZYC9gdOJNFG9HPASdLWg7YENixwX3eBXxD0qjcu70Z8GJEtBli3ZEczczMrBW4gd3iImKmpDVIDdyXJN1KamRPBQL4TkS8LKlPYbMxpOEcjwNPAJMa3O2VpOEik5Tiwr8GHNSR4zAzMzPrLhzJ0ZqGIzmamZlZd+FIjmZmZmZmS4kb2GZmZmZmnchjsK1pOJKjmdlCjtxo1n25B7sbktRH0oyurkctknpJOrmr62FmZma2NLmBbUtSL8ANbDMzM1umuIHdJCQNk3RK4flQSWdKGi5phqTpkoZU2O4YSZcUno+UNCgvz87bz5R0r6QdJT0g6RlJB+Q8PXKe8ZKmSTqxjTreIGm/wvOrJQ3OdfhzLvt/JJ2TswwDNpE0RdLwKmWeIGmCpAnz3p3V4KtmZmZm1nzcwG4eI4BDC88PBV4lhTbfFtgLGC6pdwNlrgaMiogtgbeB84G9gYOB83Ke44BZEbEDKWT68ZKqRXtcUEdJKwJ7AqVB0zsCXwa2AQ6RNAA4C3g6IvpFxJmVCoyIKyJiQEQM6LFqzwYOzczMzKw5+SbHJhERkyWtK2kDYB3gTVLj+vqImAe8IulBUiN4Wp3FzgXuzMvTgTk5suJ0UiAYgM8D20ganJ/3BDYlBZopdwdwkaSVgC8AD0XEeymWDPdExBsAkm4BdgX+VGc9AUdyNDMzs9bgBnZzuREYDKxP6i2u1pNc9CGLXokohjz/IBZGEpoPzAGIiPmSSudewKkRcVetHUXE+5IeAPYBhgA3FFeXZ6+j7mZmZmYtx0NEmssI4CukRvaNwGhgSB4nvQ6wOzCubJvngH6SlpP0cdJQjUbcBXxD0goAkjaTtFqNOh4L7MbC3nGAvSWtJWkVUlj0MaRhKWs0WB8zMzOzbs092E0kImZKWgN4MSJeknQrMBCYSuoR/k5EvCypT2GzMaThHI8DTwCTGtztlaThIpOUxnq8RmogV3M3cC3w54iYW0gfB9wMfAz4Q0RMAJA0Jk8peEe1cdhmZmZmrUQLRxCYtY+kY4ABEfHNjpQzYMCAmDBhQudUyszMzGwJkjQxIgZUWucebKsq95SPjIitytLPI93geG8b2w4FZkfET+vdnyM5mll35siLZlbiBrYtRtLWpGEgKwIbS5pCmoFkJ4CIOLuYPyKuBq5eytU0MzMza0q+ydEWExHTI6If8EXSTZTjgdUl3S1plVKAGQBJX5T0pKSJki6WNLJQ1BaFwDbfWvpHYmZmZrb0uYFttWwK/DoHq3mLFEwGAEkrA78B9o2I/qT5u4v6kqb02xE4pzRTSZEjOZqZmVmrcQPbank2Iqbk5YksDFADqQH9TESUgtJcX7bt7RExJyJeJ0WlXK+8cEdyNDMzs1bjBrbVMqewPI/Gxu13ZFszMzOzbskNHuuIp4BPSuoTEc+Roju2m0Olm5mZWStwA9vaLSLek3QycKekd0g3Q5qZmZkt09zAtqpyr/RWheeV5rS+PyL65iiQvwYm5LxDy8raqsK2ZmZmZi3HY7Cto47P82TPBHqSZhUxMzMzW2a5B9s6JCJ+Afyiq+thZmZm1izcwO5EkvoBG0TEX9ux7eyIWH0JVKtd2hPqvGz7O4HPAA9HxP71bONQ6WbW3Tg8uplV4iEinasU/dBgOHBUV1fCzMzMbGlzA7sGSX1yKPDrJD0h6SZJq0raQdIjkqZKGiepJ3AeMETSFEkVp6yTtLqkqyRNlzRNUjEy4gW5vLGS1stpX5L0mKTJku4tpA+V9LtKocgl/UjSU5IelnS9pDNy+iaS7sxhzUdL6lvna3C8pPG5bjdLWrVQ3th8LOdLml3aJiLuA96uo2xHcjQzM7OW4gZ2fT4NXBoRmwP/Ar4JjABOi4htgb2Ad4CzgRER0S8iRlQp60fArIjYOiK2AUbl9NWAsbm8h4Djc/rDwGciYjvgBuA7hbIWC0UuaQdSOPNtgX2BAYX8VwCn5rDmZwCX1nn8t0TEDrluTwDH5fSLgIsiYmvgH3WWtQhHcjQzM7NW4zHY9fl7RIzJy38AfgC8FBHjASLiXwBpprqa9gK+UnoSEW/mxbnAyLw8Edg7L38MGCGpN7AiUApLDjkUOTBHUikU+S7AnyPifeB9SX/JdVsd2Bm4sVDPleqpMLCVpPOBXsDqwF05fSBwUF7+I9Cu8dpmZmZmrcQN7PpE2fN/ASt38j4+iIjSfophxX8F/DwibpM0CBha2KaRUOTLAW9FRL921O1q4KCImCrpGGBQO8qoyZEczczMrBV4iEh9PiFpYF4+HBgL9M7DMZC0hqTlSWOO16hR1j3AKaUnkj5SI39P4MW8/NU66joG+JKklXOv9f6woJf9WUmH5P1K0rZ1lAfpmF6StAJwRCF9LGk4ChR65c3MzMyWZW5g1+cp4BRJTwAfIfUqDwF+JWkqqdG8MnA/sEVbNzkC5wMfkTQjb/u5GvseShrWMRF4vVZF87CV24BpwB3AdKB09+ARwHF5vzOBA2uVl/0IeIzUeH+ykH468O+SpgGfKuwHSaOBG4E9Jf1D0j517svMzMysW9PCUQlWiaQ+wMjuFOpb0uoRMTvP9vEQcEJETFoC+1kVeC8iQtJXgMMiot5G+2IGDBgQEyZM6LwKmpmZmS0hkiZGxIBK6zwGuzVdIWkLUq/6NR1pXNcIONMfuETprsm3gK+1dz9mZmZmrcIN7Boi4jmg4d5rSccCp5Ulj4mIUyrl70wRcXi9eSX9ADikLPnGiLigjv2MJk0H2CkcydHMugNHbzSzWtzAXkIi4irgqq6uRy25Ib2gMS3paOAMSYeSxnE/XVjXD7gcWDWnfy0i3sxBbk4CPgQej4ivSFqNNFZ9K2AFYGhE/HkpHZaZmZlZl/FNjraApC2BHwJ75KAy5T3wvwe+mwPkTAfOyelnAdvl9JNy2g+AURGxI+lGzuG50V2+T0dyNDMzs5biBrYV7UEaHvI6QET8X2lFDgXfKyIezEnXALvn5WnAdZKOJPViA3weOEvSFOAB0njwT5Tv0JEczczMrNV4iIh1hv1Ije0vAT+QtDUg4MsR8VSX1szMzMxsKXMD24pGAbdK+nlEvCFprdKKiJgl6U1Ju+WbG48CHpS0HPDxiLhf0sOkgDOlcOqnSjo1T+O3XURMbmvnjuRoZmZmrcANbFsgImZKuoDUcJ4HTAaeK2T5KnB5nv/6GeBYoAfwhzyERMDFEfGWpB8DvwSm5Ub4s+SokmZmZmatzIFmrGk40IyZmZl1F20FmvFNjmZmZmZmncgNbDMzMzOzTuQx2C1EUi/g8Ii4ND8fBJwREd1i7LMjOZrZ0uBIjGa2pLkHu7X0Ak7urMIkdeoPMEk9OrM8MzMzs2bkBnY3JunfJc3Ij9OBYcAmkqZIGp6zrS7pJklPSrpOkvK2/SU9KGmipLsk9c7pD0j6paQJwGmSDsnlT5X0UBt16SHppznvNEmn5vTnJF0oaRJwyBJ9QczMzMyagIeIdFOS+pOmyduJND3eY8CRwFYR0S/nGQRsB2wJ/BMYA+wi6THgV8CBEfGapCHABcDXcvErlu6KlTQd2CciXsxDUKo5AegD9IuID4tzaANvRMT2VY7jhLwtPdZcp7EXwczMzKwJuYHdfe0K3BoR7wBIugXYrUK+cRHxj5xnCqkR/BawFXBP7tDuAbxU2GZEYXkMcLWk/wZuaaM+ewGXR8SHsGiY9bLyFhERVwBXAKzUe1PPGWlmZmbdnhvYrW9OYXke6ZwLmBkRA6ts805pISJOkrQTKRz6REn9I+KNBuvwTu0sjuRoZmZmrcFjsLuv0cBBklaVtBpwMKm3eY06tn0KWEfSQABJK0jaslJGSZtExGMRcTbwGvDxKmXeA5xYujGybIiImZmZ2TLDDexuKiImAVcD40jjr6+MiInAmHyj4fA2tp0LDAYulDQVmALsXCX7cEnTJc0AHgGmVsl3JfACKTT6VODwdhyWmZmZWbfnUOnWNBwq3czMzLoLh0o3MzMzM1tKfJOjNUTSPsCFZcnPRsTBHS3bkRzNrL0cndHMmol7sMtI6iWp06IhVih/dhvrNpB0U43tn5O0doX0AySdtaTrGBF3RUS/ssdijetq9TQzMzNrdW5gL65iuPHODhteSUT8MyIGt3Pb2yJiWGfXyczMzMwa4wb24orhxsdLGi3pNuBxAElHShqX1/9GUo+cPlvSBTmk+FhJ6+X0jSU9mmfiOL+tHUvqk2frqBp6PDtV0qRcZt+c/xhJl+TlqyVdluvxjKRBkn4n6QlJVxf2d1hphhBJ5cM+kLR2rvt+uYyRhXWXSDqmxmv5nVz+OEmfqnLMJ0iaIGnCvHdn1SjOzMzMrPm5gb24s4Cnc7jxM4HtgdMiYjNJmwNDgF3y+nnAEXm71YCxEbEt8BBwfE6/CLgsIrZm0WiJtRRDj28DXFdY93oOPX4ZcEaV7T8CDAS+DdwG/IIUMn1rSf0kbUAaS70H0A/YQdJBpY3zD4TbgbMjor0Do2fl474E+GWlDBFxRUQMiIgBPVbt2c7dmJmZmTUP3+RY27iIeDYv7wn0B8bnEOOrAK/mdXOBUg/vRGDvvLwL8OW8fC2L3yBYTVuhx0shyycC/1Zl+79EREiaDrwSEdMBJM0kNdw3Ah6IiNdy+nXA7sCfgBWA+4BTIuLBOutbyfWFv7+oldmRHM3MzKwVuIFdWzHMt4BrIuJ7FfJ9EAsnFS+FJC/p7MnGS+HPy/dTKc98Fg2XPj9v80Eb5X9IarzvAzxYSCte8Vi5jnpGlWUzMzOzluUhIot7m+rhxu8DBktaF1I4cEkb1ShvDPCVvHxEWxnLLOnQ4+OAz+Zx1j2Aw1jYmA7ga0BfSd/Nac8DW0haSVIvUm9+LUMKfx/tvKqbmZmZNS/3YJeJiDckjck3G74HvFJY97ikHwJ3S1qO1At8CqnxWc1pwB9zQ/XPDVTlSmAzUujxD4DfksYyd4qIeClP63c/qWf+9oj4c2H9PEmHAbdJejsiLpX038AM4Flgch27+YikaaQe9MM6q+5mZmZmzcyh0q1pOFS6mZmZdRdthUp3D7Y1DUdyNLP2cBRHM2s2bmB3AUlbk2YUKZoTETvVuX0fYGREbNXJVWuYpFuBjcuSvxsRd3VFfczMzMy6mhvYXSBPmdevq+vRGSqFSTczMzNblnkWkSYhaZikUwrPh0o6U9LwHGlxuqQhFbZbEMExPx8paVBenp23nynpXkk7SnogR3c8IOfpkfOMzxEjT6xRzzMLec/NaX0kPZkjSP5N0nWS9so3i/6PpB3bKM+RHM3MzKyluIHdPEYAhxaeH0oKYtMP2JYUeGa4pN4NlLkaMCoitiRNP3g+KQDOwcB5Oc9xpIiLOwA7AMdLKh/yAYCkzwObAjvmevWXtHte/SngZ0Df/Dgc2JUUafL71SroSI5mZmbWajxEpElExGRJ6+YQ5usAb5IasddHxDzgFUkPkhrB0+osdi5wZ16eThrn/UGO7tgnp38e2EbS4Py8J6kR/SyL+3x+lKboWz3nfQF4tixa5H2FSJJ9KpRlZmZm1pLcwG4uNwKDgfVJPdoVe5LLtBVhsRhdckFEx4iYXwpgQ5oD+9Q6b0oU8JOI+M0iiemmy/JokcVIknW9zxwq3czMzFqBh4g0lxGkqI+DSY3t0cCQPE56HWB3UgTGoueAfpKWk/Rx0vCNRtwFfEPSCgCSNpO0Wht5vyZp9Zx3w1JUSzMzMzNL3IPdRCJipqQ1gBdzpMVbgYHAVFL48u9ExMu5x7hkDGk4x+PAE8CkBnd7JWkIxyRJAl4DDqpSv7slbQ48mrIyGzgSmNfgPs3MzMxaliM5WtNwJEczMzPrLtqK5OghIi1MUj9JX+zqepiZmZktSzxEpLX1AwYAf21ko45Gmmyj3OUj4sNq6x0q3czaw6HSzazZuAe7k9QTbEXSWpL+lIO0jJW0Td52qKRrJI2W9Lykf5P0nzm4zJ2FGxD7S3pQ0kRJd5XmxM7BYy6UNC7vezdJK5Lmuh4iaYqkIXk/ZxTqPCPXe5G6A2eR5q9+hzSX9gnVGte5jr2UvCHp6Jz+e0l750A4t0kaBdy35M6AmZmZWXNwA7tz1Qq2ci4wOSK2yc9/X9h2E2AP4ADgD8D9EbE18B6wX25k/woYHBH9gd8BFxS2Xz4idgROB86JiLnA2cCIiOgXESM6WPdqxgC7AFsCzwC75fSBwCN5eftc78+Wb+xIjmZmZtZqPESkc9UKtrIR8GWAiBgl6aOS1szb3lEIAtODRQPE9AE+DWwF3JNn8OgBvFTY9y3570TaF9ilvYFiRpOmD3weuAw4QdKGwJsR8U6u6z0R8X+VNo6IK4ArAFbqvanvuDUzM7Nuzw3szlUr2MoHtbbNQWDKA8QsTwryMjMiBtbY9zyqn9e2gtK0N1DMQ8ApwCeAH5DCsA8mNbxL3mljezMzM7OW4gb20jUaOAL4saRBwOsR8a/cy1vLU8A6kgZGxKN5yMhmETGzjW3eBtYoPH8O2B9A0vbUFymyTRHxd0lrAytGxDOSHiYNK/lmo2U5kqOZmZm1Ao/BXrqGAv0lTQOGAV+td8M8pnowcKGkqcAUYOcam90PbFG6yRG4GVgrDwH5JvC3xg+hoscKZY0GNgQe7qSyzczMzLoVB5qxpuFAM2ZmZtZdONCMmZmZmdlS4jHYVhdJxwKnlSWPiYhTuqI+ZmZmZs3KQ0S6IUm9gMMj4tIlVP7siFi9yrpBwBkRsX8d5fQBdo6IP9az35V6bxq9v/rLBmpqZq3EERnNrDvxEJHW0ws4uTxRUrNdkehDClhjZmZmtsxwA7t7GgZskmcHGZ9DrN8GPA4g6cgcNn2KpN9I6pHTZ0u6QNLUHKp9vZy+saRHc9jz8+uthKQdJE2WtImkz+b9Tclpa+R67pbTvl2lDEdyNDMzs5biBnb3dBbwdET0A84khSI/LSI2k7Q5MATYJa+fR5p7G2A1YGxEbEsKEHN8Tr8IuCyHZi9Gh6xK0s7A5cCBEfE0ae7rU/I+dyOFeD8LGJ1Dtf+iUjkRcUVEDIiIAT1W7dngy2BmZmbWfNzAbg3jIuLZvLwn0B8YL2lKfv7JvG4uMDIvF0Oq7wJcn5evrWN/m5PCm38pIl7IaWOAn0v6FtArIj5s57GYmZmZdWvNNmbX2qcYilzANRHxvQr5iiHYy0OqN3K360ukMOvbAf8EiIhhkm4HvgiMkbRPA+UBjuRoZmZmrcE92N1TeQj0ovuAwZLWBZC0lqSNapQ3BvhKXj6irYzZW8B+wE/yrCJI2iQipkfEhcB4oG+NepqZmZm1JDewu6GIeIPUSzwDGF627nHgh8DdOST7PUDvGkWeBpwiaTopzHk9dXgF2B/4taSdgNMlzcj7/AC4A5gGzMs3VVa8ydHMzMys1XgebGsaDpVuZmZm3YXnwTYzMzMzW0p8k2OTy9EQR0bEVjXyLIiYKGkAcHREfEvSMcCAiPimpJOAdyPi9zn97oj4Z5Uyt2bxGUXmRMRO1SI9SnqAFOVxQln6gjq0dazTX5xFn7NubyuLmbUoR3E0s1biBnZr6EOKmPhHgNzAXWysRURcXnh6DDCDPAtIhbzTgX6dXE8zMzOzluchIl1A0jBJpxSeD5V0pqTh+UbB6ZKGVNiuT47aOCk/ds6rFomYKGmQpJEVth8q6QxJg4EBwHV5m/0k/amQb29Jt9Y4hl9IminpPknrFFYdlcucIWnHBl8aMzMzs27PDeyuMQI4tPD8UOBVUo/xtsBewHBJ5bN/vArsHRHbk6I1XpzTa0ZMLIqIm0g93EfkyIt/BfoWGsrHAr9ro4jVgAkRsSXwIHBOYd2qucyTa5QBOFS6mZmZtR43sLtAREwG1pW0gaRtgTdJjevrI2JengLvQWCHsk1XAH6bp9O7Ediik+oTpPHWR0rqBQwkTbNXzXzSjwSAPwC7FtZdn8t8CFgzl9fWvh0q3czMzFqKx2B3nRuBwcD6pMbqxnVs823gFVIv93LA+51Yn6uAv+Qyb2ww1HlUWa70vCpHcjQzM7NW4B7srjOCFD1xMKmxPRoYIqlHHqqxOzCubJuewEsRMR84CuiR09sTMXGRbfJsIv8kBam5qsa2y+V6Q7q58uHCuiEAknYFZkWEx32YmZnZMsU92F0kImZKWgN4MSJeyjcVDgSmknp9vxMRL+cp+EouBW6WdDRwJ/BOTl8QMRG4GphcRxWuBi6X9B4wMCLeA64D1omIJ2ps+w6wo6QfksaFF2/IfF/SZNJwlq/VUQ8zMzOzluJIjraApEuAyRHxX12xf0dyNDMzs+6irUiO7sE2IL1JSD3T/6+r62JmZmbWnbmB3Q0sjWiOEdG/QpmPASuVJR+Vg9BUq8fpwBUR8W4Dhwg4kqNZd+Poi2ZmlbmB3Tr60PnRHHdqRz1OJ03dt1gDW1KPiJjXjjLNzMzMug3PItJFuns0R0mX5QAxMyWdm9O+BWwA3C/p/pw2W9LP8g2YA9v1YpmZmZl1I25gd53uHs3xB3lg/zbAZyVtExEXk3rDPxcRn8v5VgMei4htI+Lh8kIcydHMzMxajRvYXaQFojkeKmkSaUrALduoxzzg5jb260iOZmZm1lI8BrtrdctojpI2Bs4AdoiINyVdDaxcpcz36x137UiOZmZm1grcg921ums0xzVJU/rNkrQesG+1Ms3MzMyWNW5gd6GImElqjL4YES8Bt5KiMk4FRpGjOZZtdinw1XzTYF8qRHOU9O06q3A1KZrjFEmr5LTrgL+3Fc0xIqaShoY8SZq1ZExh9RXAnaWbHM3MzMyWNY7kaIvoymiOjuRoZmZm3YUjOVpdHM3RzMzMrOPcwLYFOiuaY3s5kqNZ9+AIjmZmbWuJMdiSekk6eQmWP3sJlt1H0uGdXOYASRfXzrnINkMlnVGeHhE75bm1i49Ob1ybmZmZtYqWaGADvfj/7N1pmF1VgfXx/yLMsyhg0JYgEpmJJIABoqAgNrQKmpcoY9B2jDLYoDi8EhXaIK00ooCRZhQwLwpCMyNjiAwREjIAYiPQigyiiIaZZL0f9i64Kapu3Uoqlaqb9XuePDlnn7OHU+HDrsM+e8FrJtiSBvQb+jq+YZSI8z5j+ze2D+3LNiMiIiKiNe0ywZ4EbFx3w5heo8QvBe4BkHSApDvq9R9LGlLL50k6ru68cVvdcg5JG0m6tcaVH9usY0k/k7RXw/lZksZKWlnSmbWNGZJ2rdfHS7pU0vXAdbw24nxIjUufLmmWpE/XevtIuk7FUEn3S3pjN2N6JSa9vpk+Q9KNkn5f48w77vtabecW4O09POeNkk6q45wjaftavo6kX9ax3iZp61r+3VdiYAAAIABJREFU7nrvzPr8XW7dlyTHiIiIaDftMsE+GnigRn4fBWwLHGZ7uKTNKJHiO9Xr84H9a73VgNtsbwPcDHyylp8EnGp7K+DRHvp+JfJc0orAe4HLgQmUgMStgI8BZ0vqCGPZFhhr+928NuL8E8DTtrejpDh+UtJGti+uY5kA/AQ4post/LqzKbAHsD1wjKQVJI2k7ME9AtiT1yZGdmXV+jP8HK/GqH+TsuvI1sBXgXNq+ZHAhHr/GOC5rhpMkmNERES0m3aZYHd2h+0H6/F7gZHAdEkz6/lb67UXgcvq8Z2U5RoAOwEX1ONze+jrSmBXSStRAldutv0csDPwUwDb9wEPA8NrnWtt/7Wb9t4HHFTHejvwemCTeu0LwFeAF2xf0E39rlxu+wXbTwJPAOtTJr0X237W9t+BS1to54L6PDcDa9ZI9Z2pPyPb1wOvl7QmZW/s79c35mt3lwoZERER0W4G9BrlxfBMw7GAs21/pYv7XvKrG4HPZ+GfR0sbhNt+XtKNlDfE44Cf9XJ8nQn4gu2ru7j2ZmABsL6k5WqaYyteaDju/Jy90fln0u3PyPYkSZdT3o5Pk7RH/UWjW4lKj4iIiHbQLm+wm8VzXweMlbQevLJmeMMe2ptGWT4Bry4naWYKcAjlrfBVtWxqR11Jw4G3AL9tYexXA5+VtEJHXUmr1Q8iz6AsN7kX+GIL42rmZmBvSavU9dEfaKHOuDqmnSnLWJ5m4efcBXjS9t8lbWx7tu3jgemUZSoRERERba8t3mDb/oukaZLmUNb6Pt5w7R5JXweukbQc8BJlHfPDTZo8DDhf0peBS1oYwjWUZRKX2H6xlp0CnCppNvAyMN72C5I6130l4pwSXX4SZanKXSo3/xnYmxL+MtX2LfXe6ZIubxZp3oztuyRNocSyP0GZBPfkeUkzgBWAj9eyicAZkmYBzwIH1/LD64edC4C5lKU0EREREW0vUenRkroM5kjbSyzLPFHpERERMVioSVR6uywRiYiIiIgYENpiiUh/kLQVr91R5AXbOyyN8QBI+jBlSclfatHqwGq2hy5Gmz+i7KLS6CTbu/RQ70bqG25JDwGj6q4lLUtUesTAkkj0iIhFkwl2i2o8+IilPY5O7qJ8bDgCXvnI8DVx562StLztCX00ts5ti7IkqdWdTyIiIiIGpUywBxFJX+TVjwtPB95JTbAErqUE3Kwu6efAlpS9vQ+w7Ros833KW+4nKR9dPlrfPM+k7Gd9gaT/BY6hbOf3tO13dTOWVYAzgW2A+4BVurhnGGVXlNspe5HvSaePSyV9CvgUwJA11+31zyQiIiJioMkEe5CoE+RDgB0oe2XfDhwAbNnpDfY7gC2AP1G2G9xJ0u3AycCHbP9Z0jjgOF6drK/YsUi/7nqyh+1HapBMdz4LPGt7sxqPflc3920CHGz7tq4u2p4MTAZYaegm+eI2IiIiBr1MsAePnSnJi88ASLqIsu92Z3fY/mO9ZyZly7+/Ud5oX1u3CRzCwhHwUxqOpwFnSfp/wEVNxvMu4AcAtmfVbfq68nB3k+uIiIiIdpQJdvvpKrVRwFzbo7up80qypO3PSNoB2Au4U9JI23/ppl4rmqVWLiRJjhEREdEOsk3f4DGVkry4qqTVgH0ob5u7S7Bs9FtgXUmjASStIGmLrm6sCYy32/4GJeTmn7pp82Zgv1pnS2DrXj1NRERERJvKG+xBoiYvngXcUYtOt31nQ4LllZSPHLuq+6KkscAPJK1F+Xf/T0rCYmcnSNqE8tb7OkrSY1dOBc6UdC8luv3ORXy0iIiIiLaSJMcYMJLkGBEREYNFkhwjIiIiIvpJlogMMHXv6Mtsb9nDPTvaPr+ejwIOsn2opPGUFMXPS/oMZSu9c2r5Nbb/1Mvx7AFcAcxuKH7Q9j69aacVSXKMWPqS3hgRsfgywR6chlE+MDwfwPZvgNesrbB9WsPpeGAOZX/sltm+WtJzHXttR0RERERzWSLSDyRNkjSh4XyipKMknSBpjqTZNfylc71hkqZKuqv+2bFemgSMkTRT0hGSdpF0WRf1J0o6sn7gOAo4r9bZS9IvG+7bXdLFLTzHGyTdKmmvev7lOva76zOuJ+nOem0bSZb0lnr+gKRVu2jzU5J+I+k38599uqchRERERAx4mWD3jynAvg3n+wJPACMoUeO7UXbvGNqp3hPA7ra3BcZRg12Ao4GptkfYPrGnzm3/nPKGe//6JvoKYFNJHdnkhwBnNGtD0vqUXUq+YftySf8MfAjYwfY2wHdtPwGsLGlNSgjObyi/CGwIPGH72S7GNtn2KNujhqy6Vk+PEhERETHgZYlIP7A9o77d3QBYF3iKMrm+wPZ84HFJNwHbAY2JiCsAP5Q0ghIaM7yPxmNJ5wIHSDoTGA0c1KTKCpQt+ybYvqmW7Qac2TFptv3XWv5rYCdK0uO/A++nbPk3tS/GHhERETHQZYLdfy4ExgJvpLzR3qiFOkcAj1Peci8HPN+H4zkT+O/a5oW2X25y78uUfa73AG5qch+UAJoxwIbAJcCXAdPNHt2NkuQYERER7SBLRPrPFOCjlEn2hZQ3uuMkDalLNd7FqyEyHdYCHrW9ADgQGFLL/0FrCY6NFqpTdxP5E/B1ymS7GQMfpywr+XItuxY4pGNdtaR1avlU4ADgd3XcfwX2BG7p5XgjIiIiBqVMsPuJ7bmUCe4jth8FLqYsB7kbuB74ku3HOlU7BThY0t3ApsAztXwWML9+XHhEi0M4CzitfuS4Si07D/iD7XtbGP984GPAeyR9zvZVwKXAbyTNBI6s9z1EWRJyc616C/A320+1OM6IiIiIQS1JjsswST8EZtj+r6U9FkiSY0RERAwezZIcswZ7GVW303sG+LelPZaIiIiIdpIJ9gDUH2mOtkd20ebtwEqdig+0PVvSPNurL/7TdS9JjhFLThIaIyL6TybYg9cw+jjN0fYOfT3IiIiIiGVNPnLsJ8tKmmMtu1HSSbWfOZK27+3PKyIiImKwygS7/ywTaY4Nt69a+/lcs3YTlR4RERHtJhPsfmJ7BrCepA0kbUOnNEfbj1NCXLbrVHUF4CeSZlP2z968j8ZjoCPNcW1KmuOVTap0pDl+yfa1tay7NEeAC2rZzcCatY+uxpGo9IiIiGgrWYPdv5aVNEco4TTNzl8jSY4RERHRDvIGu38tK2mOUJazIGln4GnbWf8RERERy4S8we5HtudKeiXNsX5UOJqS5mhqmmPdgq/DKcAvJB0EXEUXaY6UlMYZLQzhLEqa43PAaNvPUdIc1201zVHSx4BLJf3D9imSRlDSHF+krOv+ar39eUkzKEtLPt7C2CIiIiLaQpIcl3FLIs1R0o3AkXXrwJYlyTEiIiIGiyQ5RpeS5hgRERHR9zLBbiN1ucYGtq9o5f7epjm2Og7bu0gaI2ku8BKwF3CS7bHN6iXJMWLJSIpjRET/ygS7vYyghMm0NMHuSh+mOe4PfMf2T+t508l1RERERLvILiKLqCYs3ifpLEn3SzpP0m6Spkn6naTtJa0j6ZeSZkm6TdLWte5ESWfXhMaHJX1Y0ndrIuJVklao942UdJOkOyVd3RFCU5MSj5d0R+17jKQVgW9RdiWZKWlcR4pjw5jn1HH3OPYmz93j2CX9KyVI59u17WGS5izJf4+IiIiIgSIT7MXzNuB7wKb1z37AzsCRlN00vkn5gHDren5OQ92NgfcAHwR+CtxgeyvgOWCvOsk+GRhbl3KcARzXUH9529sDhwPH2H4R+AYwpaY7TlnMsTfTdOy2TwcuBY6yvX+zhpLkGBEREe0mS0QWz4Mda5PreuPrbLumLg4DNgQ+AmD7ekmvl7RmrXul7ZfqvUMoW/ABdNR9O7AlcK0k6j2PNvR9Uf37znp/X4+9mZ7G3jLbk4HJACsN3SRb2kRERMSglwn24nmh4XhBw/kCys/2pZ7q2l4g6SW/ul9iR10Bc22P7qHv+XT/7/gyC/9fipV7MfZmehr7IkmSY0RERLSDLBFZsqZSPvZD0i7Ak7b/3mLd3wLrShpd668gaYse6nROd3wI2LbW35bWotkjIiIiYjFkgr1kTQRGSpoFTAIObrViXVM9Fji+pjXOBHbsodoNwOYdHzkCvwDWqUtAPg/c3/tHiIiIiIjeSJJjDBhJcoyIiIjBolmSY95gR0RERET0oXzkGF2SdAhwWKfiacDBtldfEn0myTGibyXBMSJi6cgEO7pk+0zgzM7lklpeRx4RERGxLMoSkVgkKk6o6ZCz60eVSPqRpA/W44slnVGPPy7puGZtRkRERLSDTLBjUX0YGAFsA+wGnFCj3KcCY+o9bwI2r8djgJs7N5Ikx4iIiGg3mWDHotoZuMD2fNuPAzcB21En2JI2B+4BHq8T79HArzs3Ynuy7VG2Rw1Zda1+HH5ERETEkpE12NGnbD8iaW3g/ZQ31usA+wLzbP+jWd0kOUZEREQ7yBvsWFRTgXGShkhaF3gXcEe9dhtwOGWCPRU4sv4dERER0fbyBjsW1cWUZR93Awa+ZPuxem0q8D7b/yPpYcpb7EywIyIiYpmQJMcYMJLkGBEREYNFkhwjIiIiIvpJJtgREREREX0oa7AHGEnDgMtsb9nDPTvaPr+ejwIOsn2opPHAKNufl/QZ4Fnb59Tya2z/aRHGNK+reHRJNwJH2u6TdR2JSo/oO4lJj4hYejLBHpyGAfsB5wPUCe5rJrm2T2s4HQ/MAXo9wY6IiIiI1mWJSD+QNEnShIbziZKO6ipqvFO9YZKmSrqr/tmxXppECXOZKekISbtIuqyL+hMlHSlpLDAKOK/W2UvSLxvu213SxT08w4mS5kq6rm7L1+HA2uYcSds39HuupFsl/U7SJ5u0myTHiIiIaCuZYPePKZSwlQ77Ak/QddR4oyeA3W1vC4wDflDLjwam2h5h+8SeOrf9c8ob7v1tjwCuADZtmCgfApzRpInVgN/Y3oKS2HhMw7VVa5uf69TG1sB7KFv5fUPSBt2MLUmOERER0VYywe4HtmcA60naQNI2wFOUyXVXUeONVgB+Imk2cCGweR+Nx8C5wAE1dXE0cGWTKgsovyQA/JQSk97hgtrmzcCatT2AS2w/Z/tJ4AZg+74Ye0RERMRAlzXY/edCYCzwRspkdaMW6hwBPE55y70c8HwfjudM4L9rmxfafrkXdd3NceN5d+XdSlR6REREtIO8we4/U4CPUibZF9I8arzDWsCjthcABwJDavk/gDV62f9CdepuIn8Cvk6ZbDezXB03lI8rb2m4Ng5A0s7A07Y7FlJ/SNLKkl4P7AJM7+V4IyIiIgalvMHuJ7bnSloDeMT2o/WjwtdEjdct+DqcAvxC0kHAVcAztXwWMF/S3cBZwIwWhnAWcJqk54DRtp8DzgPWtX1vD3WfAbaX9HXKuvDGDzKflzSDspzl4w3lsyhLQ94AfHtRtgeMiIiIGIwSlb4Mk/RDYIbt/+rjdicC82z/R2/qJSo9IiIiBotEpS+jJI2QtGc31+6k7PTx0/4dVURERER7yxKR9jaCsv/1FZ0v2B7ZuUzS7cBKnYoPtD27N53antib+zskyTGiZ0lojIgY+PIGu4/UUJj7JJ0l6X5J50naTdK0GrayvaR1JP1S0ixJt0nautadKOnsGirzsKQPS/puDaC5StIK9b6Rkm6SdKekqzv2zZZ0o6TjJd1R+x4jaUXgW5QPKWdKGtcRPNMw5jl13MMk3QfcC6wKzAWOpKy9vqgjQKab596+BsrMkPRrSW+v5StLOrM+wwxJuy6Zn3xERETEwJIJdt96G/A9YNP6Zz/KntFHAl8FvklZ87x1PT+noe7GlGCWD1KWbdxgeyvgOWCvOsk+GRhb3z6fARzXUH9529sDhwPH2H4R+AYwpQbSTKG5nsbenfuAMbbfUfv791o+gbLl9lbAx4CzJa3cuXKSHCMiIqLdZIlI33qwYzmFpLnAdbZdg2KGARsCHwGwfb2k10tas9a90vZL9d4hlF1DADrqvh3YErhWEvWeRxv6vqj+fWe9v6/H3p21KJPnTSi7oaxQy3em/EKA7fskPQwMp+wu8grbk4HJACsN3SRf3EZERMSglwl233qh4XhBw/kCys/6pZ7q2l4g6SW/ur1LR10Bc22P7qHv+XT/7/oyC/9fi8Y3yj2NvTvfprxt36duMXhjk3sjIiIi2l4m2P1rKrA/8G1JuwBP2v57fSPdk98C60oabfvWumRkuO25Tep0DqR5CPgXAEnb0lqaZE/WAh6px+Mbyjue9XpJw4G31GfoVpIcIyIioh1kDXb/mgiMlDQLmAQc3GrFuqZ6LHB8DZiZCezYQ7UbgM07PnIEfgGsU5eAfB64v/eP8BrfBb5Tw2Yaf2E7BViuLjGZAoy3/UJXDURERES0kwTNxICRoJmIiIgYLBI0ExERERHRT7IGO1oi6RDgsE7F02xPWBrjiYiIiBioskSkjdVdPS6zveVS6PsEYE9KiuQDwLO2z2lWZ6Whm3jowf/ZH8OLGFCSzhgRMfg0WyKSN9ixpHwKWMf2/KU9kIiIiIj+lDXYg4ykSZImNJxPlHSUpBNq9PnsumNI53rjJf2w4fyyulUgkubV+nMl/arGn98o6feSPljvGVLvmV6j3j/dZIyXAqsDd3YV0R4RERHRzjLBHnymAPs2nO8LPAGMALYBdgNOkDS0F22uBlxvewvK3tnHArsD+wDfqvd8Anja9nbAdsAnJXW5j7btDwLPtRLRnqj0iIiIaDdZIjLI2J4haT1JGwDrAk9RJtcX1OUYj0u6iTIJntWkqUYvsnA0+wsNse3Davn7gK0lja3nawGbAA8u5vMkKj0iIiLaSibYg9OFlNCZN1LeaLeSyNgsJr1zNHtjbHvHfyMCvmD76sUZeDNJcoyIiIh2kCUig9MU4KOUSfaFlFjycXWd9LrAu4A7OtV5CBghaTlJ/wRs38s+rwY+WyPakTRc0mqL8QwRERERbSlvsAch23MlrQE8YvtRSRcDo4G7AQNfsv1Y3aavwzTKco57gHuBu3rZ7emU5SJ3SRLwZ2DvxXmOiIiIiHaUfbBjwEhUekRERAwWiUqPiIiIiOgnWSLSBUkjgA1sX9HP/Q4DdrR9/iLWfwgYZfvJRay/AfAD22N7vLncvxVwbqfiF2zv0Om+8XVcn2/W3uxHnmbY0Zf3YsQRg0sSGyMilg2ZYHdtBDCKEvPdn4YB+wGLNMFeXLb/RPlwstX7Z1N+VhERERFRLfUlIpKGSbpP0lmS7pd0nqTdJE2T9LuaKriOpF/WBMHbJG1d606UdLakqZIelvRhSd+taYZXNex4MVLSTZLulHR1RwhLTSs8XtIdte8xklakhKuMkzSzqyTCmpg4rJWxN3nud9f2Z0qaUT9anASMqWVHSFpZ0pn1eWZI2rXWHSLpP+o4Zkn6QkPTX5B0V62zaW/6r88zp15fVdL/k3SPpIsl3S5pVL02T9Jxku6u/x7r1/IP1PtmqCRCrr9I/1FEREREDGJLfYJdvQ34HrBp/bMfsDNwJPBV4JvADNtb1/NzGupuDLwH+CDwU+AG21sBzwF71Un2ycBY2yOBM4DjGuovb3t74HDgGNsvAt8AprSSRNjC2LtzJDDB9ghgTB3v0cDU2u+JwATA9Xk+BpwtaWXgU5S33SPqz+S8hnaftL0tcGrtozf9N/oc8JTtzYH/C4xsuLYacJvtbYCbgU/W8luAd9p+B/Az4EtN+geS5BgRERHtZ6AsEXmwLjdA0lzgOttuSBLcEPgIgO3rJb1e0pq17pUNqYNDWDiRcBjwdmBL4NqyuxxDgEcb+r6o/n0nr6YW9uXYuzMN+L6k84CLbP+xjq/RzpRfDrB9n6SHgeGUOPTTbL9cr/21m+f58GL0vzNwUm1/jqTGVMgXgcsa+tm9Hr8ZmFL/D8GKtJDymCTHiIiIaDcDZYL9QsPxgobzBZQxvtRT3Zo62DmRcHlKAuFc26N76Hs+3f88mqUg9jT2LtmeJOlyYE9gmqQ9uru3l1p5nu76f77FPhp/zo39nAx83/alknYBJvZm4ElyjIiIiHYwUJaI9GQqsD9Anbg9afvvLdb9LbCupNG1/gqStuihzj+ANRrOHwK2rfW3pbVo8qYkbWx7tu3jgemU5SWd+2187uHAW+rzXAt8WjXGXNI6fdR/o2nAvvXezYGtWmh2LeCRenxwb8cUERER0Q4GywR7IjCyLlOYRC8mb3VN9VjgeEl3AzOBHXuodgOwecdHjsAvgHXqEpDPA/f3/hFe4/COjxQpb+ivBGYB8+vHg0cApwDL1eUmU4Dxtl+gpCr+LzCrPtN+fdR/o1Mov5jcAxwLzAV6WiQ9EbhQ0p3AIm0VGBERETHYJckxuiRpCLCC7eclbQz8Cnh7/YVliUiSY0RERAwWapLkOFDWYMfAsypwQ92FRcDnluTkOiIiIqJdZIK9hEk6BDisU/E02xMGcv+2/0EJ2+k3SXKMdpUEx4iIZUsm2L0gaW1gP9untFrH9pnAmS22P8/26os6vj7ofzwtRJo33D/P9urqFLEu6QJgi9rvlZQ9sU3Zi/yB3j9FRERExOAxWD5yHCjWpgSwLKRjN49lle0/NUyu3whsZ3vrGpazN/Bz2+/I5DoiIiKWBZlg984kYOO6u8h0lYj2S4F7ACQdoBK7PlPSj+uHgs2ixTeSdGuNNT+2WceSdlGJe79E0u8lTZK0f+1vdv0Qsdu4ckmr69XY9VmSPlLLD1GJeb8D2KmHMXQ5XjVErAPXAG+qP4NjKAmZn5V0QzdtJskxIiIi2kom2L1zNPBAjRc/irI39mG2h0vaDBgH7FSvz6fuYU330eInAafWKPTGdMnubAN8BtgMOBAYXmPeTwe+UO/pLq78/wJP296qxqtfXxMXv0mZWO8MbN5D/62M94PUn5HtbwKnASfa3rWrm21Ptj3K9qghq67V0/NHREREDHjL9NKGPnCH7Y448PcCI4HpNXJ8FeCJeq27aPGdqBHwwLnA8T30N932owCSHqC8LYYSC98xge0urnw34KMdDdl+StLewI22/1zbnEKJYu9Ob8fbK0lyjIiIiHaQCfbieabhWMDZtr/SxX3dRYtD+fivVa3Esi9WXHkLsnF6RERERBNZItI7naPMG10HjJW0HpT4ckkb9tDeNF59q7x/sxt7obu48muBV7bmk/Q64Hbg3ZJeX/e7/j89tL0kxhsRERHRVjLB7gXbfwGm1Q/6Tuh07R7g68A1NX78WmBoD00eBkyoUehv6qNhTqTruPJjgdfVePS7gV3rcpOJwK2UyfO9S2G8EREREW0lUekxYCQqPSIiIgaLZlHpeYPdxiSNkLTn0h5HRERExLIkHzkOMJK2ouzQ0egF2zssQnMjKHHnV/RyDF/jteuxL7R93CKMoWWJSo92laj0iIhlS95g95EatnKfpLNqcMt5knaTNE3S7yRtXz98/GUNerlN0ta17kRJZ0uaStnO71uULfiGAI9R9qhG0sgaNnOnpKvrVnxIulHS8TV05n5JYyStWNsZV0NfxtV+jmwY85w67oXGTtkP+0jKLimrAZ/qbnLdXZv1+Iv1fI6kw/v0Bx4RERExQGWC3bfeBnwP2LT+2Y8yOT4S+Col1GVGDXr5KnBOQ92NgfdQglp+CtxQA12eA/aqu3ycDIy1PRI4A2ic9C5fQ2cOB46x/SLwDWBKDX2Zsphj7xVJI4FDgB2AdwKflPSOLu5LkmNERES0lSwR6VsP2p4NIGkucJ1t1103hgEbUoNabF9ft8dbs9a90vZL9d4hwFW1vKPu24EtgWtrkM0QFk5TvKj+fWe9v6/H3ls7Axfbfqa2eREwBpjReJPtycBkgJWGbpIvbiMiImLQywS7b/UUBPNST3VtL5DUGEzTUVfAXNuje+i7c5BNo5dZ+P9arNyLsXenWZsRERERy5xMsPvXVEpAy7dryuKTtv9e30j35LfAupJG2761LhkZbntukzqdg3EeAv4FQNK2wEa9f4TX6K7NqcBZkiZRfjnYBziwWUOJSo+IiIh2kDXY/WsiMLIG0Uxi4aTFpuqa6rHA8TUoZiawYw/VbgA27/jIEfgFsE5dAvJ54P7eP8JrdNmm7buAs4A7KImRp9ue0V0jEREREe0iQTMxYCRoJiIiIgaLBM1ERERERPSTrMGOlkg6BDisU/E02xOWxngiIiIiBqosERmkapjLZba3XMpD6ZGkK4D9bP+t2X0rDd3EQw/+z34aVcSSkdTGiIhlQ7MlInmDHUuc7T2X9hgiIiIi+kvWYA8gkiZJmtBwPlHSUZJOqHHjs+tuIJ3rjZf0w4bzy+o2gEiaV+vPlfSrGtl+o6TfS/pgvWdIvWd6jXH/dJMx7lLr/7zGq5+n4v2SLux032X1+CFJb+imvSQ5RkRERFvJBHtgmQLs23C+L/AEMALYBtgNOEHS0F60uRpwve0tKPtiHwvsTtmX+lv1nk8AT9veDtiOEmvebI/sd1Ai2TcH3grsBPwK2EHSavWeccDPehqc7cm2R9keNWTVtXrxWBEREREDUybYA0jdJ3o9SRtI2gZ4ijK5vsD2fNuPAzdRJsGtepGFY9dvsv0Sr0awA7wPOEjSTMqe1a8HNmnS5h22/2h7AWU/7mG2X679fEDS8sBewCW9GGdEREREW8ga7IHnQkqgzBspb7RbSVtsFlfeOXa9MZK9499fwBdsX93iGBtj1Ruj2X9GCZv5K/Ab2/9osT0gSY4RERHRHvIGe+CZAnyUMsm+kBI5Pq6uk14XeBclHbHRQ8AISctJ+idg+172eTXw2Rq/jqThDUs9euMmYFvgk7SwPCQiIiKiHeUN9gBje66kNYBHbD8q6WJgNHA3YOBLth+r2/R1mAY8CNwD3Avc1ctuT6csF7lLkoA/A3svwtjn1w8bx9OLGPiIiIiIdpJ9sGPASFR6REREDBaJSo+IiIiI6CdZItI+Fu4wAAAgAElEQVRLktampBKesoTan2d79SXRdi/HsRVwbqfizYA32X5ySfQ5+5GnGXb05Uui6YglLgmOERHRIRPs3lsb+Byw0ARb0vJ1q7q2YHs2ZYvAV0h6aOmMJiIiImLwyBKR3psEbCxpZk0+nCrpUsoHhkg6QNId9fqPJQ2p5fMkHSfpbkm3SVq/lm8k6daa0nhss44lDZV0c217jqQxDW33lNY4rI71rvpnx9720+meL9ZrcyQdXsuOknRoPT5R0vX1+D2SzuumryQ5RkRERFvJBLv3jgYesD0COIqyLd1htodL2oySYLhTvT4f2L/WWw24zfY2wM2UrewATgJOtb0V8GgPfe8HXF3b3oYS8tLRdk9pjU8Au9veto7xB4vQDwCSRgKHADsA76QkP76DsqVgx2R8FLB63fpvTH3m10iSY0RERLSbLBFZfHfYfrAevxcYCUwvu92xCmViCyVR8bJ6fCdlAgwlZvwj9fhc4PgmfU0HzqiT1l/a7pj4dk5rfMH2S5Ia0xpXAH4oqWPiP3wR+umwM3Cx7WcAJF1EmUSfCoyUtCYljOYuykR7DHBok/4iIiIi2kYm2IvvmYZjAWfb/koX9zUmKjamH0LZ37pHtm+W9C5KDPlZkr5v+xxaS2s8Anic8kZ6OeD5Reinp/G9JOlByj7YvwZmAbsCb6Psz91UkhwjIiKiHWSJSO/9A1ijm2vXAWMlrQcgaR1JG/bQ3jRKciO8upykS7Wtx23/hBIOs23Lo4a1gEdtLwAOBIYsRj9Tgb0lrVoTH/epZR3XjqQsCZkKfAaY0fALQERERERbywS7l2z/BZgmaQ5wQqdr9wBfB66RNAu4FhjaQ5OHARPqco439XDvLsDdkmZQ1lGf1IuhnwIcLOluYFMWfvPeq35s3wWcRYlsvx043faMenkq5Zlvtf045U35VCIiIiKWEUlyjAEjSY4RERExWCTJMSIiIiKin+QjRwZeOmM3KYov2N6hj8e1FWUN+MOUDy977EfSMOAy21v25VggSY4xOCXBMSIiOssEuxhQ6YxdpSguwX7WXNL9RERERCxLskSkWJrpjLtIuknSJTV5cZKk/Wt/syVtXO/7gKTbJc2oaY0dfb27jmtmvbZGK0mMDf0/JOkNNenxXkk/qYmQ10hapd4zsj7j3cCEhrpDaoLkdEmzJH26lu8j6ToVQyXdL+mNi/HvExERETFoZIJdLM10Rih7U38G2Iyyhd5w29tTtsj7Qr3nFuCdtt8B/Az4Ui0/EphQxzYGeI4ekhib2AT4UU2E/BuvBuCcCXyhPmejTwBP294O2I6S6LiR7Yvrc08AfgIcY/uxrjpMVHpERES0mywR6Vp/pjMCTLf9KICkB4BravlsSlALwJuBKZKGAisCHeObBnxf0nnARbb/KKmnJMbuPNhw753AsLo+fW3bHVHn5wL/XI/fB2wtaWw9X4sySX+Q8ovBHMovIBd016HtycBkgJWGbpItbSIiImLQywS7a/2Wzli90HC8oOF8QUObJwPft32ppF2AiQC2J0m6HNiTsj/3HouaxNhpHPMpv0w0I8qb7au7uPbmOv71JS1XA26aSpJjREREtIMsESmWWjpjL6wFPFKPD+4olLSx7dm2jwemA5suZuLjQmz/DfibpJ1rUePzXA18tr4pR9JwSavVePYzgI9RItK/uKj9R0RERAw2eYNNSWeU1JHO+BzweMO1eyR1pDMuB7xEWVv8cJMmDwPOl/Rl4JI+GuZE4EJJTwHXAxvV8sMl7Up5WzwXuJIyuT9K0kvAPOCgxez7EMqSE/Pq8hUok/dhwF0q62f+DOwN/Bsw1fYt9cPI6ZIut33vYo4jIiIiYsBLkmMMGElyjIiIiMEiSY4REREREf0kS0T6SX+lMzbp/3ZgpU7FB9awmQEhSY6xtCWVMSIi+sKgn2APtJjz7nSVzljDXfazff7itt/Q5ijgINuHduq/Wfz5RGCe7f/oq3FERERELKvaYYlIR8z5QupOFgNWHd8wSihMn7H9m86T64iIiIjoP+0wwV6aMec/k7RXw/lZksZKWlnSmbWNGXWXDySNl3SppOsp2/9NAsbUsR3RF9HjKtHrl9XjiZLOkHSjSgz7oQ33fa22cwvw9h6e80ZJx9ef4/2q0etNxrucpFMk3SfpWklXNITRdG47SY4RERHRVtphgr00Y86nAPsCSFqRkvp4OWUbP9c2PgacLWnlWmdbYKztd9exT7U9wvaJ9EH0eBc2BfYAtgeOkbSCpJGUrfxGUAJqtmuhneVrfPvhwDG1rMvxAh+mvJ3fnBL9Prq7Rm1Ptj3K9qghq67V4iNFREREDFwDehnFIurPmPMrgZMkrQS8H7jZ9nM1lOVkANv3SXoYGF7rXGv7r920t9jR41243PYLwAuSngDWB8YAF9t+FqC+8e/JRfXvOymT52bj3Rm4sKY3PibphlYGmiTHiIiIaAftOMHut5hz289LupHyhngc8LNejq+zPo0erzrHny/qv3lHO41tdDleSXsuYh8RERERg147LBFZ2jHnUyhJh2OAq2rZ1I66koYDbwF+28LY+yt6/GZgb0mrSFoD+MAittPleCk/w4/UtdjrA7ss5ngjIiIiBo1B/wZ7AMScX0NZSnKJ7Rdr2SnAqZJmAy8D422/UJepNJoFzFeJEz+Lsv57GEs4etz2XZKmAHdTlsxMX5R26D4q/ReU5Tn3AH8A7gLyBWNEREQsExKVHkuEpNVtz5P0euAOyoemTT/MTFR6REREDBZqEpU+6N9gx4B1mUoI0IrAt1vZ9SRJjtEfktYYERFLWjuswV7iJG1V96pu/HN7H7a/tqTXhOX0UGePLsZ0cTf3zmuxzR910eYhvRlXB9u71O0HN69t/3BR2omIiIgYbPIGuwVdxZz3sY40yoXi3iUtb/vlbsZ0NeUjwz5je0JfthcRERGxLMob7IFhaaZR7iLpJkmX1LTHSZL2r/3NlrRxve8Dkm5XSab8VUNfq+vV1MpZkj5Syw9RSX28g7K3eHf9J8kxIiIi2kom2APD0kyjBNgG+AywGSV5cXhNbTydEnADcAvwTtvvoOz3/aVa/n8paY5b2d4auF7SUOCblIn1zpRExy4lyTEiIiLaTZaIDEz9mUYJMN32owCSHqBsPQgwG9i1Hr8ZmFInzytS0iUBduPVfcOx/ZSkvYEbbf+5tjmFV5MsIyIiItpaJtgDU7+lUVaNaY8LGs4XNLR5MvB925dK2gWY2Iv2W5Ko9IiIiGgHWSIyMCztNMpWrAU8Uo8Pbii/lhLeQx3f64DbgXdLen1Nefw/fTSGiIiIiAEvE+wBwPZfgI40yhM6XbsH6EijnEWZ0A7tocnDgAk1SfJNfTTMicCFku4EnmwoPxZ4naQ5NWVy17rcZCJwK2Wyv0iJkxERERGDUZIcY8BIkmNEREQMFs2SHPMGOyIiIiKiD+Ujx2WEpK0oO4o0esH2DktjPF1JVHosaYlJj4iI/pAJ9jKiN2mUkr4IfLyeng78EriKshXgtsBc4CDbz0oaCXwfWJ2yNnu87Ucl3Uj52HFXSlLlJ2xP7bsnioiIiBiYskQkFlInzIcAOwDvpITXvA54O3CK7c2AvwOfqzuEnAyMtT0SOAM4rqG55WtgzeHAMd30lyTHiIiIaCt5gx2d7QxcbPsZAEkXAWOAP9ieVu/5KXAo5a32lsC1NQRnCAsnR15U/74TGNZVZ7YnA5MBVhq6Sb64jYiIiEEvE+xoVefJrykhOHNtj+6mTkdgTecQnIiIiIi2lUlPdDYVOEvSJMoEeh/gQOAkSaNt3wrsB9wC/BZYt6O8LhkZbnvuonScJMeIiIhoB1mDHQuxfRdwFnAH5SPF04GnKJPpCZLupazJPtX2i8BY4PgaMjMT2HFpjDsiIiJioEjQTPRI0jDgMttbLsl+EjQTERERg0WCZiIiIiIi+knWYEePbD9E2S0kIiIiInqQCfYA0coyjHrPjrbPr+ejKIEvh0oaD4yy/XlJnwGetX1OLb/G9p8WYUzzbK/eRfmNwJG2+3Q9R5IcY0lLkmNERPSHTLAHl2GUHTzOB6gT3NdMcm2f1nA6HpgD9HqCHRERERG9lzXYS5CkSZImNJxPlHSUpBMkzZE0W9K4LuoNkzRV0l31T8fOHJOAMZJmSjpC0i6SLuui/kRJR0oaC4wCzqt19pL0y4b7dpd0cQ/PcKKkuZKuk7Ruw6UDa5tzJG1f750taW0Vf5F0UC0/R9Lu3bSfJMeIiIhoK5lgL1lTgH0bzvcFngBGANsAuwEnSBraqd4TwO62twXGAT+o5UcDU22PsH1iT53b/jnlDff+tkcAVwCbNkyUD6HEm3dnNeA3trcAbmLhuPNVa5ufa2hjGrATsAXwe0oCJMBo4NfdjHGy7VG2Rw1Zda2eHikiIiJiwMsEewmyPQNYT9IGkrah7Cc9ArjA9nzbj1Mmrtt1qroC8BNJs4ELgc37aDwGzgUOkLQ2ZeJ7ZZMqCyi/JECJR9+54doFtc2bgTVre1OBd9U/pwJbSXoT8FRH9HpEREREu8sa7CXvQkoYyxspk9WNWqhzBPA45S33csDzfTieM4H/rm1eaPvlXtR1N8cd5zcDE4C3AF+jpECOpUy8e5Qkx4iIiGgHeYO95E0BPkqZaF5ImWyOkzSkLtV4FyU1sdFawKO2F1BiyofU8n8Aa/Sy/4Xq1N1E/gR8nTLZbma5Om54NR69wzgASTsDT9t+2vYfgDcAm9j+fb3/SMrEOyIiImKZkAn2EmZ7LmWC+4jtR4GLgVnA3cD1wJdsP9ap2inAwTV+fFOgY3nFLGC+pLslHdHiEM4CTqsfJK5Sy84D/mD73h7qPgNsL2kO8B7gWw3Xnpc0AzgN+ERD+e3A/fV4KvAmFp6YR0RERLS1RKUvgyT9EJhh+7+W9lgaJSo9IiIiBotmUelZg72MkXQn5c30vy3tsURERES0o0ywB4m6S8d+tk9ZnHZsj+yi7dsp+2XPbig+0Pbszvf2MMaJwDzb/7EoY0uSYywJSW+MiIj+lgn24LE2Zc/phSbYkpbv5U4gr2F7hxqLPmJx2omIiIiIfOQ4mEwCNq4fK06vSY+XAvcASDpA0h31+o8lDanl8yQdVz+MvE3S+rV8I0m31vTFY5t1LGn1muR4V73/Qw3Xvibpfkm3AG+vZcvXMe5Sz78j6bgl8UOJiIiIGGgywR48jgYeqG+ZjwK2BQ6zPVzSZpRt83aq1+cD+9d6qwG32d6Gsl3eJ2v5ScCptrcCHu2h7+eBfWqy5K7A92oc+kjKFoQjgD2pgTn1jfp44FRJuwHvB77ZVcOJSo+IiIh2kyUig9cdth+sx+8FRgLTJQGsQolbB3gRuKwe3wnsXo93Aj5Sj88Fjm/Sl4B/l/QuSrrjm4D1KVHoF9t+FqC+UQfK9oSSzq19j7b9YlcN254MTAZYaegm2dImIiIiBr1MsAevxuhxAWfb/koX973kV/dinM/C/+atTmj3B9YFRtp+SdJDwMot1NsK+BuwXiudJMkxIiIi2kGWiAwezVIcrwPGSloPQNI6kjbsob1plOUd8Opyku6sBTxRJ9e7Ah1t3wzsLWkVSWsAH+ioIOnDwDqUpMqT6y4oEREREW0vE+xBwvZfgGk1VfGETtfuoUSfXyNpFnAtMLSHJg8DJkiaTVny0cx5wKh670HAfbXfuyhR8HcDVwLTASS9gfJR5r/avh/4IWXNd0RERETbS5JjDBhJcoyIiIjBolmSY95gR0RERET0oXzkGK+QtBVlR5FGL9jeoT/6T5JjLK6kNkZExECQCXYv9FVceZP259lefUm03Yoajd5tmuPSHl9ERETEYJAlIr3TEVe+EEn5RSUiIiIigEywe2upxZXX+4+q/c6S9M1aNkzSfZLOqpHl50naTdI0Sb+TtH29b/va1wxJv5b09ib9bNHwHLMkbdLpuiSdIGlOHfu4Wv4jSR+sxxdLOqMef7y7qPQkOUZERES7yQS7d5ZaXLmk9wGbANtTlnGMrMmKAG8DvgdsWv/sB+wMHAl8td5zHzDG9juAbwD/3qS7zwAn1ecYBfyx0/UP1zFsA+wGnCBpKDCVku4IZeu/zevxmPrcr2F7su1RtkcNWXWtZj+CiIiIiEEhSxsWT3/Glb+v/plRz1enTLj/F3iwrp9G0lzgOtuu+1YPq/evBZxd30YbWKFJX7cCX5P0ZuAi27/rdH1n4ALb84HHJd0EbEeZYB8uaXPKW/3X1Yn3aODQJv0BSXKMiIiI9pAJ9uLpz7hyAd+x/eOFCqVhwAsNRQsazhc09PVt4Abb+9Q6N3bXke3zJd0O7AVcIenTtq/vaYC2H6kfgr6f8sZ6HWBfYJ7tf/RUPyIiIqIdZIlI7yzNuPKrgY9LWr22/6aOvlq0FvBIPR7f7EZJbwV+b/sHwCXA1p1umQqMkzRE0rqUOPQ76rXbgMMpE+yplGUqU3sxzoiIiIhBLRPsXliaceW2rwHOB26t9/+c7if7Xfku8B1JM+j5/1zsC8yRNBPYEjin0/WLgVmUiPTrgS/Zfqxemwosb/t/gLsob7EzwY6IiIhlRqLSY8BIVHpEREQMFs2i0rMGOwaMJDlGK5LWGBERA12PS0QkHdZKWfQNSVvV/acb/9xe97ue08d97dFFXxf3cR+HSrpX0nl92W5ERETEQNXKG+yDKfs1NxrfRVn0ge7iyuvOH33d19WUjyeXpM8Bu9nuvJd2RERERFvq9g22pI9J+m9gI0mXNvy5Afhr/w2xfUmaJGlCw/nEmtb4mpTETvXGS/phw/llknapx/Nq/bmSflUTHG+U9PuGlMUh9Z6OVMhP9zDOL9ex3C1pUi27UdKJNYXxXknbSbqopkceW+85DXgrcKWkI7ppO0mOERER0VaavcH+NSVd8A2UlMAO/6DsIBGLbwrwn8CP6vm+lLCZ91FSEt9ACa7pMgWxG6sB19s+qi73OJYSbLM5cDZwKfAJ4Gnb20laibIzyjUNoTmvkPTPwIeAHWw/K2mdhssv2h5VlwxdQgna+SvwgKQTbX9G0vuBXW0/2dVgbU8GJgOsNHSTfHEbERERg163E2zbDwMPU1L4YgmwPUPSepI2ANYFnqIsD+kqJbHVX2peBK6qx7OBF2y/1CnV8X3A1pLG1vO1KKmQr5lgU6LQz7T9bB1z4/+9uLShn7m2HwWQ9Hvgn4C/tDjmiIiIiLbR4xpsSe8ETgY2A1YEhgDP2F5zCY9tWXEhMBZ4I+WN9kYt1HmZhZf3rNxw3Jga+Uqqo+0Fkjr+vQV8oa7BXhyNiZGd0yR7vUNNotIjIiKiHbQSNPND4GPA74BVgH/l1SUNsfimUNIcx1Im281SEjs8BIyQtJykfwK272WfVwOflbQCgKThklbr5t5rgUMkrVrvXaeb+yIiIiKCFt8y2v4fSUPqsoUzaxrgV5bs0JYNtudKWgN4xPajdd30aEpKoqkpiZ12EZlGWc5xD3AvJTGxN06nLBe5S5KAPwN7dzO+qySNAH4j6UXgCuCrvewvIiIiYpnRY5Jj/cBuN8qk7DHKh4/jbW+z5IcXy5IkOUZERMRg0SzJsZUlIgfW+z4PPEP5eO0jfTe8iIiIiIj20eMbbABJqwBvsf3bJT+k9iRpbWA/26csofbn2V69m2u7AEfa/pcm9bcCzqV8yLoq8DfKDiQ79GIMv7a9Y68G3mCloZt46MH/uajVYxmQmPSIiBgoFusNtqQPADOpW79JGiHp0ua1ogtrU1INF9Kws8dSZXu27RGUMc6xPaI3k+vaxiJPriMiIiLaRStLRCZSdqn4G4DtmbS2lVwsbBKwsaSZNUFxav1F5R4ASQdIuqNe/7GkIbV8nqTjaoribZLWr+UbSbq1Jiwe2+ogauLiDEkbS3p37W9mLVujjnNMLesufXGLhrHOkrRJx1jr38tJOkXSfZKulXRFw57bndtKkmNERES0lVYm2C/Z7jzzSeJe7x0NPFDfEh8FbAscZnu4pM2AccBO9fp8YP9abzXgtvpR6c3AJ2v5ScCptreifHjaI0k7AqcBH7L9AHAkMKH2OQZ4ro5zan2DfWI3TX0GOKnWGwX8sdP1D1N2Kdmcsoa/27Ai25Ntj7I9asiqa7XyGBEREREDWisT7LmS9gOGSNpE0smUGPVYPHc0RJO/lxIzPl3SzHr+1nrtReCyenwnr6Yx7gRcUI/PbaG/zSiR5B+w/b+1bBrwfUmHAmvbfrnFsd8KfFXSl4ENbT/X6frOwIW2F9h+DLihxXYjIiIiBr1u1/9KOtf2gcADwBaUpL4LKCEl3+6f4bW1ZxqOBZxtu6u9xRuTGeez8L9Zb/5PwqOUxMd3AH8CsD1J0uXAnsA0SXu00pDt8yXdDuwFXCHp07av78VYupQkx4iIiGgHzd5gj5S0AWXpwveAPYD31eNV+2Fs7eYfwBrdXLsOGCtpPShpiZI27KG9aZQESHh1OUkzf6NMiL9TdxVB0sb148bjgenApj2Mk1rvrcDvbf8AuATYuouxfaSuxV4f2KWF8UVERES0hWY7WJxGmfi9FWhM/xDlzelbu6oUXbP9F0nTJM2hrHV+vOHaPZK+DlwjaTngJWAC8HCTJg8Dzq/LNC5pcQyPS/oX4EpJHwcOkLQrsACYC1xZj+dLuhs4q5t12PsCB0p6iRI+9O+drv+CsszlHuAPlKTJfMEYERERy4RWkhxPtf3ZfhpPtAlJq9ueJ+n1wB2UDzgfa1YnSY4RERExWDTbB7vHPZgzuY5FdFkN11kR+HZPk+uIiIiIdjEgQk7ahaRhwGW2t1xK/XekMTbqVRpjp/b2AI7vVPyg7X16qmt7l972N/uRpxl29OW9rRbLiKQ4RkTEYJEJdhuxPRsY0YftXU3ZNSYiIiIiWtTKPtjLNEmTJE1oOJ8o6ShJJ0ia8//Zu9Mwu6oy/f/fm4BMQRAZDE4BBMMcM9GMBkHpbhxAA1FABm1aBBHpDooj0ZYWpG1bQEC0IaiIaYQogkyCQIhAYsjM5F+GdogM/lA7CAGS5/9iPSfZOakzVKiqVJ26P9eVi33W3ms4B16sLNZedyYpTuyi3nGSLqh8vq5yeseSrL9I0s8ljZN0u6RHJL07nxmSz8zKtMSPNBnjMEl3ZrLiQkn7daOf4ZkqeV/+aRp3nt+9NqYvVtp4UNIUSQ9LukLSQflS568ljWvSnpMczczMrKN4gt3aVMqpGTVHAE9SVor3AA4CzpU0rBttbgzcFhG7UI7F+zLwduAw4Ev5zIeBv0TEWGAscIKkRhH1RwI3ZbLiHsDcbvTzJPD2iBhFOZLxvEaDlvQOYAdgXH7/0ZL2z9tvohzhOCL/HEkJnJkEfKZRm05yNDMzs07jLSItRMQcSVvlmeBbAs9QJpdXRsQy4AlJd1AmwfPbbPYF4Ma8XkDZJ/2ipAWsTGp8B7C7pAn5eVPK5PZRVjcLuFTSesCPI6I2wW6nn/WACyTVItp3bDLud+SfOfl5aI7pfyl7sxcASFoE3BoRUdeXmZmZWcfzBLs9VwETgNdQVrQbrSRXvcSq/4dgg8p1NZ1xOSUlk4hYLqn270TAKbkPuqmIuDNXkg8Bpkj6z4j4bpv9nEY5k3uPHO/zTboS8JWI+NYqheXlzqWVouWVz8tp878zJzmamZlZJ/AWkfZMpaQmTqBMtqcDE3Of9JbA/pSznqseA0ZmmuHrKdsquuMm4KO5Ko2kHSVt3NWDmfr4RER8G/gOMKob/WwKLI6I5cAHgSEtxvQhSUOz39fW0ifNzMzMrPAKdhsiYpGkTYDfR8RiSdOAvYB5lFTLT0bEH3Mlt2YGZTvH/cADlDTD7vgOZWvFfZIEPAUc2uDZ8cDpmay4BDimG/1cCFwt6RjKdpJnGz0YETdL2gm4uwyJJcDRlK0lZmZmZkYbSY5mfcVJjmZmZjZQNEty9BYRQ9Ivu/n8eEnX9dZ4zMzMzAYybxEZQHo6qbEmIlY5+7q3+mnFSY7WiFMczcxsIPEEewDp6aTGGklLImJoBuFMBp6mnHoyGzg6j9v7e0kPAn8D7qrU3Rg4H9iVcuTf5Ij4iaTTgN0i4kM5Yb8SGBcRf+vp8ZuZmZn1J94iYvXeAnwC2BnYDthH0gbAt4F3AaMpxxXWfJYSZjMOOIASurMx8A3gTZIOAy4DPuLJtZmZmQ0GnmBbvZkR8bs8tm8u5SSTEZQgmV/nudrfrzz/DuAMSXOB2ykr32/I+sdRtprcEREzuurMUelmZmbWabxFxOpVA2OW0fq/EQHvi4iHuri3A+Uov20aVY6IS4BLANYftoOPtDEzM7MBzxNsa8eDwHBJ20fEb4APVO7dBJwi6ZTcq/2WjJffFDiPEsJzgaQJEfGjZp04ydHMzMw6gbeIWEsR8Tzwz8D1ku4Dnqzc/jfKy43zJS3KzwBfB74ZEQ8DHwbOduqjmZmZDQYOmrF+w0EzZmZmNlA4aMbMzMzMrI94gm1mZmZm1oP8kuMakrQZcGREXNhL7S+JiKG90XZ/5SRHa8RJjmZmNpB4BXvNbQacVF8oqV/8pUWF//2amZmZ9TFPwNbc2cD2kuZKmiVpuqRrgfsBJB0taWbe/5akIVm+RNJZkuZJukfS1lm+raS7JS2Q9OVmHUsaKulWSffl8+/J8uGSHpL0XWAh8HpJH8hnFko6p9JGo/Iux9dgHFMkTajWzX+Ol3SnpOtzPBd7sm9mZmaDhSc9a+4M4DcRMRI4HRgFnBoRO0raCZgI7JP3lwFHZb2NgXsiYg/gTuCELP8GcFFE7AYsbtH388BhETGKEk/+NUnKezsAF0bELsCLwDnA24CRwFhJh0rapqvyFuPrrnHAKZTI9e2B93b1kJMczczMrNN4gt1zZkbEo3l9IDAamJUR4gcC2+W9F4Dr8no2JYocYB/gyrz+Xou+BPy7pPnAz4HXArWV5scj4p68HgvcHhFPRcRLwBurZTAAACAASURBVBWU4JdG5c3G110zI+KRiFiW32vfrh6KiEsiYkxEjBmy0aZr2JWZmZlZ/9Ev9gt3iGcr1wIuj4hPd/Hci7Hy8PH6KPJ2DyU/CtgSGB0RL0p6DNigi3GsiWbjq/cS+Ze03ALyisq9+u/S8rs5ydHMzMw6gVew19z/AZs0uHcrMKGWXChpc0lvbNHeDOD9eX1UsweBTYEnc3J9ANCo7ZnAWyVtkXvAPwDc0aS8ux6jrNQDvJuS6FgzLveVr0PZLnPXGrRvZmZmNuB4gr2GIuJPwAxJC4Fz6+7dD3wOuDm3cdwCDGvR5KnAyZIWULZ8NHMFMCafPQZ4sMEYF1P2iv8CmAfMjoifNCpv0WdXvk2ZqM8D9mLV1fNZwAXAA8CjwLQ1aN/MzMxswHFUuvU4SeOBSRHxzu7Uc1S6mZmZDRSOSjczMzMz6yNewe7HJO3G6ieKLI2IPft4HJ8FDq8rvioizurJftYftkMMO/a/erJJ6+ec0GhmZgNVsxVsnyLSj0XEAknHAdtExM/6sm9Jw4G9I+IHOZHu1mQ6TzYZExFP9/zozMzMzPovbxHp/0YC/7gW+h0OHLkW+jUzMzMb0DzBrsio8QczAvxhSVdIOkjSDEm/ljQuj9z7saT5GSW+e9adLOnyjEx/XNJ7JX0148hvlLRePjda0h2SZku6SdKwLL9d0jkZr/6wpP0kvQL4EjAxI9cnZj+TKmNemONuOfYm3/ut2f5cSXMkbUKJgt8vy06TtIGky/L7zMnjAZE0RNJ/5DjmSzql0vQpWhnnPqJB305yNDMzs47iCfbq3gR8DRiRf46kpBBOAj4DfBGYExG75+fvVupuT4kffzfwfeAXGX3+HHBITrLPByZExGjgUlbderFuRIwDPgGcGREvAF8ApkbEyIiY+jLH3sgk4OSMdd8vx3sGMD37/TpwMhD5fT4AXC5pA+CfKavdI/M3uaLS7tMZ535R9rEaJzmamZlZp/Ee7NU9GhELACQtAm6NiMgzp4dTQl3eBxARt0l6taRXZt0bMvxlATAEuDHLa3XfDOwK3CKJfGZxpe9r8p9rGlHeauyNzAD+U9IVwDUR8bscX9W+lL8cEBEPSnoc2BE4CLg4I9eJiP/X4Pu8dw2+j5mZmdmA4wn26pZWrpdXPi+n/F4vtqobEcslVSPHa3UFLIqIvVr03SyifEU8edqgct1q7F2KiLMlXU/Z6z1D0sGNnu2mdr7PCo5KNzMzs07gLSLdN52MMs9Alacj4q9t1n0I2FLSXll/PUm7tKhTH8n+GDAq648Ctm175A1I2j4iFkTEOZQExhFd9Fv93jsCb8jvcwvwEUnr5r3NX+54zMzMzAYyT7C7bzIwOiPQzwaObbdi7qmeAJyT8eJzgb1bVPsFsHPtJUfgamDz3ALyMeDh7n+F1Xyi9pIiZYX+BmA+sEzSPEmnARcC6+R2k6nAcRGxFPgO8L/A/PxOPnnEzMzMBjUHzVi/4ah0MzMzGygclW5mZmZm1kc65iVHSZsBR0bEhb3U/pKIGNpLbQ8nUxN7sM0xwDER8fFK2fHAqXWPzoiIk/P+ZGBJRPxHm2O+LiJ2rfYlaX3gemAL4CvAH4CLKVtP9oqI5xq1ueD3f2H4Gde3/yVtwHJEupmZdbKOmWADmwEnUfYKryBp3doRcv1Rvhw4nLJ3uccm2BHxK+BXdWWXAZf1VB8N+npLlo0EkHQx8JWI+H5P92tmZmbWH3XSFpGzge3zZcBZmah4LXA/gKSjMyVxrqRvSRqS5UsknZUv890jaess31bS3ZlC+OVmHUv6oaRDKp+nSJrQJP3wOEnXSroNuJXVUxOHSDo3v8d8SR/JeodJulXFsExsfE2DMY2XdF1eT5Z0qUpa5COSqqvan8127qKc093se47O32keJXhmlb4kbUUJ2Bmb3+UjwBHAv+UZ21216SRHMzMz6yidNME+A/hNrpyeTjnK7tSI2FHSTsBEYJ+8v4w8cg7YGLgnIvYA7gROyPJvABdlcmE1DKYrUykTSVTizQ+kbJNolH5Ijm9CRLyV1VMTPwz8JSLGAmOBEyRtGxHTciwnA9+mpD3+sc3fZwRwMDAOODOPCBwNvB8YSTkDe2yLNi4DTsnfajUR8STwT5Xv8i3gWuD0iDiqQR0nOZqZmVlH6aQJdr2ZEfFoXh8IjAZmSZqbn7fLey8A1+V1NUFxH+DKvP5ei75uAA7I/cf/ANyZe433pazoEhEPArX0Q4Bb6lIPq94BHJNjvRd4NbBD3jsF+DSwNCKubFC/K9dHxNKIeBp4EtiaEos+LSL+lmd5X9uocu5x3ywi7syiVr+JmZmZ2aDUSXuw6z1buRZweUR8uovnqomL9YmDbZ1hGBHPS7qdskI8EfhhN8dXT5SV4pu6uPc6SjLj1pLWiYjl7YyRVVMe20pW7GtOcjQzM7NO0Ekr2PXJg1W3AhNyjzCSNpf0xhbtzaBsn4CV20mamQocT1kVvjHLGqUfthr7TcBHJa1Xqytp43wh8lLKdpMHgH9pY1zN3AkcKmlDSZsA72r0YET8GfizpH2zqJ3fxMzMzGzQ6XermGsqIv4kaYakhcBzwBOVe/dL+hxws6R1KEfGnUzZstHIqcAPJH0K+EkbQ7iZsm3iJ5nYCOVEk4sy/fAlMv1QUn3dFamJwBTK/u/hwH0qDz8FHAr8K2V/81357CxJ10fEA22MbzURcZ+kqcA8yraRWS2qHA9cKiny+5qZmZlZHSc5Wr/hJEczMzMbKJzkaGZmZmbWRzpmi0hfkLQbq5+esTQi9uzFPoeTiYkN7h8MfB3YCPhzFj8DLMhkxeOAMRHxMUknAn+LiO9m+c0R8Ycu2vwm5RSVqm9kUE39syvab/Id2krBdJJj53OCo5mZDQaeYHdDRCygnBndb0TETZJOAiZFxDtbPHtx5eNxwEJKlHn9cyfXl5mZmZlZe7xFZC2QdLakahLiZEmnZ3rjwkx+nNhFveGZUHlf/tk7b9UnQa5IcayrP1nSJEkTgDHAFVnnEEk/rjz3dknTmoz/+Ex/nEllpVvS1pKm1dIeK+Nr9ls4ydHMzMw6iifYa8eK5Md0BOUUj5HAHsBBwLmShtXVexJ4e0SMopy3fV6W1ydBNhURPwJ+BRyVyZY/A0ZI2jIfOZ5yHOBqckxfpEys9wV2rtw+D7gjkx5HAYvaGIuTHM3MzKyjeIK9FkTEHGArSdtI2oOyZ3okcGVELIuIJ4A7WD26fD3g23ns31WsOrl9OeMJyt7yozOxcS9KOmVX9gRuj4in8jjCqZV7bwMuyjaXRYSXpM3MzGzQ8R7stecqYALwGsokdds26pxGOd97D8pfjp7vwfFcBvw027wqIl7qwbbb4iRHMzMz6wRewV57plKSIidQJtvTgYmShuRWjf2BmXV1NgUWZzz6B4EhWd4sxbKRVerkaSJ/AD5HmWw3ci/wVkmvzqTJwyv3bgU+CpDfw3s+zMzMbNDxBHstiYhFlAnu7yNiMTCNkug4D7gN+GRE/LGu2oXAsZniOAJ4NstXJEFKOq3NIUwBLs6XHDfMsiuA3zZLhsyxTgbupsTJV589FTggt7DMpoe2sJiZmZkNJE5ytBUkXQDMiYj/Xhv9O8nRzMzMBopmSY7eg21A+Y+EsiL+r2t7LGZmZmYDmSfYHUzSSGCbiPhZq2cjYnQX9e8F1q8r/mAG7tSeGQ+8EBG/bDKOMcAxEfHxZmNwkmPncoKjmZkNJp5gd7aRlECZlhPsrrQZAT8eWAI0nGBHxK8o526bmZmZdTy/5NhDMmXxQUlTMuXwCkkHSZoh6deSxknaXNKPJc2XdI+k3bPuZEmXZ0rj45LeK+mrmeh4Y57WgaTRku6QNFvSTbUgGkm3SzpH0szsez9JrwC+RDmZZK6kibUkx8qYF+a4W4690XcGTgROyz72k3R4tjtP0p35XJfJkmZmZmadyBPsnvUm4GuUEz5GAEdS0g4nAZ+hJCDOiYjd8/N3K3W3pwS1vBv4PvCLiNgNeA44JCfZ5wMTcjvHpcBZlfrrRsQ44BPAmRkC8wVgaiY8VgNh1mTsq4mIx4CLga9nH9Ozz4MzzfHdLfp0VLqZmZl1HG8R6VmP1vYnS1oE3BoRkcfWDQfeCLwPICJuy7OkX5l1b4iIF/PZIcCNWV6r+2ZgV+AWSeQziyt9X5P/nJ3P9/TY2zUDmCLpfypjaigiLgEuAVh/2A4+0sbMzMwGPE+we9bSyvXyyufllN/6xVZ1I2K5pBdj5fmJtboCFkXEXi36Xkbjf68vser/tdigG2NvS0ScKGlP4BBgtqTVXp5sxEmOZmZm1gm8RaRvTQeOghWnbzwdEX9ts+5DwJaS9sr660napUWd+oTHx4BRWX8U7cWzt7JKH5K2j4h7I+ILwFPA63ugDzMzM7MBwxPsvjUZGC1pPnA2cGy7FXNP9QTgnExynAvs3aLaL4Cday85AlcDm+cWkI8BD3f/K6zmp8BhtZccgXPz5cyFlJNF5vVAH2ZmZmYDhpMcrd9wkqOZmZkNFM2SHL2CbWZmZmbWg/ySYy+TtBlwZERc2EvtL4mIoQ3ujQcmRcQ722hnOLB3RPygwf3jgVPrircE3hMRv5L0M8r3/LOkjwMfBe4DPgRcD2wBfKXZcYFOcuw8TnA0M7PByBPs3rcZcBKwygRb0roR8dLaGVKXhlPOvu5ygh0RlwGXVcsk3V65/4+VWycBB0XE7yT9Xd4f2cPjNTMzM+uXvEWk950NbJ8vAc7KtMZrgfsBJB2dCYxzJX1L0pAsXyLprExEvEfS1lm+raS780XCL7c7CEljJc2RtL2kt2Z/c7Nskxznfll2WoM2NpT0Q0kPSJoGbFi595ikLSRdDGwH3CDpU5TQnLHZ7vZr9AuamZmZDSCeYPe+M4Df5Aru6ZRj8k6NiB0l7QRMBPbJ+8vIY/yAjYF7MhHxTuCELP8GcFGmPFaDZhqStDclcfE9EfEbSjrjydnnfpS0yDOA6ZnI+PUGTX0U+FtE7AScCax2xnVEnAj8ATggIs4B/qnS7m+6GJuTHM3MzKyjeILd92ZGxKN5fSBlkjpL0tz8vF3eewG4Lq+r6Yz7AFfm9ffa6G8nSlLiuyLif7NsBvCfuVd6s25sVdmfsiJNRMwH5rdZr6GIuCQixkTEmCEbbfpymzMzMzNb67wHu+89W7kWcHlEfLqL56ppjvXpjN05W3ExJbHxLZSVZSLibEnXA/8IzJB0cDfa6zVOcjQzM7NO4BXs3lefplh1KzBB0lYAkjaX9MYW7c0A3p/XRzV7MP2ZElv+lTxVpJa2uCC3cMwCRrQYZ82dlBchkbQrsHsb/ZuZmZkNKp5g97KI+BNllXghcG7dvfuBzwE3Z7rjLcCwFk2eCpwsaQHw2jbH8ATwTuCbkvYEPiFpYfb5InADZbvHsnypssuXHIGLgKGSHgC+RNm6YmZmZmYVTnK0fsNJjmZmZjZQOMnRzMzMzKyP+CXHDiBpN1Y/UWRpROy5hu0dDJxTV/xoRBy2Ju21y0mOncUpjmZmNlh5gt0BImJBvsC4IpJd0nhJ17UTk95FezcBN63peDLhcVJEeL+HmZmZDTreItI5apHsPUJSj//lq5ZSaWZmZtbJPMEeoCT9S54EslDSJ1g1kr12WslQST+S9KCkKyQp646WdIek2ZJukjQsy2+X9F+SfgWcKunwbH+epDubjKVZhPoSSV+TNA/Yq4u6TnI0MzOzjuItIgOQpNHA8cCelLCae4GjgV0z/pzcMvIWYBdKwMwMYB9J9wLnU2LTn5I0ETgL+FA2/4raG7F5FODBEfF7SZs1GdKKCHVJuwP3Ve5tDNwbEf/aVcWIuISSNMn6w3bwkTZmZmY24HmCPTDtC0yLiGcBJF0D7NfFczMj4nf5zFxK3PqfgV2BW3JBewgl7bFmauV6BjBF0v8A1zQZz/7AeVAi1PN87ZplwNXtfCknOZqZmVkn8AS7sy2tXNfi1gUsiojVtmukFVHuEXFiBtMcAsyWNDqDc7rj+YhY1s06ZmZmZgOW92APTNOBQyVtJGlj4DDKanOrqHOAh4AtJe0FIGk9Sbt09WBGqt8bEV8AngJe36BNR6ibmZmZJa9gD0ARcZ+kKcDMLPpORMyWVItkvwHo8kDpiHhB0gTgPEmbUv4b+C9gURePnytpB8qq963AvAZDugi4LCPUH8AR6mZmZjaIOSrd+g1HpZuZmdlA4ah0MzMzM7M+4i0ivSCPtFuRqtgL7S+JiKG90XaLfns1Qt1R6Z3DMelmZjaYeYLdO2qpiqtMsCWtGxEvrZ0hrbnauF9uhLqZmZnZYOAtIr2jmqo4S9J0SdcC9wNIOlrSzLz/rVqEeKYenpXJifdI2jrLt5V0t6QFkr7crGNJ4yXdKel6SQ9JuljSOpKGSJqSyYwLJJ2Wz5+QY5wn6WpJG2X5lKx7L/DVBn1NljSp8nmhpOH5p5Ye+UCmSW7UoA0nOZqZmVlH8QS7d5wB/CZTFU8HRgGnRsSOknYCJgL75P1lwFFZb2PgnojYg3L03QlZ/g3goojYjVVDYRoZB5wC7AxsD7wXGAm8NiJ2zXYuy2eviYix2ecDwIcr7bwO2Dsi/qX7PwFvBi6MiJ2Av1JW9FcTEZdExJiIGDNko03XoBszMzOz/sUT7L4xMyIezesDgdHArExXPBDYLu+9AFyX17MpyYsA+wBX5vX32uzvkQx4uZKS/PgIsJ2k8yX9PWXSC7BrrrAvoEz0q2diX/UyQmJ+GxEz8vr7OQYzMzOzjuc92H3j2cq1gMsj4tNdPPdirDw3sZa8WNOd8xTrn42IeEbSHsDBwInAEcCHgCnAoRExT9JxwPgG4+7KS6z6l7QNmo2h1aAdlW5mZmadwCvYveP/aJyqeCswQdJWAJI2l/TGFu3NAN6f10c1ezCNy33b61C2o9wlaQtgnYi4GvgcZdsKOc7FktZrs+2qx2rtSBoFbFu594ZaWiQl5fGubrZtZmZmNiB5gt0LIuJPQC1V8dy6e/dTJrg3S5oP3AIMa9HkqcDJuY3jtW0MYRZwAWVP9aPAtKx3e25L+T5QW0H/PHAvZRL/YBttV10NbC5pEfAx4OHKvYdyzA8Ar6KkPZqZmZl1PCc5dhhJ44FJEfHOtTiG4cB1EbFrd+o5ydHMzMwGCic5mpmZmZn1Ea9gD1CSJlBSFf+vUrw0IvZso263kiAlHU/ZplI1IyJObreNdqw/bIcYdux/9WST1gec2mhmZoNRsxVsnyIycA0FboiIj/V2RxFxGSvPzTYzMzOzJrxFpB9plIAoaaykX2ba4kxJmwJfAiZmGuTEBu0NlXRZJjfOl/S+yr2uEiPfJeleSXMk/bxSPlnSpZJul/SIpI9X2vl8JkbeJenKWrKjpO0l3Shpdp6zPaLBGJ3kaGZmZh3FE+z+pz4B8WPAVEoS5B7AQZTzqb8ATI2IkRExtUFbnwf+EhG7RcTuwG1Z3igx8i7g7yLiLcAPgU9W2hpBOUN7HHCmpPUkjQXeB+wB/ANQ/d8klwCnRMRoYBJwYVcDdJKjmZmZdRpvEel/6hMQPwssjohZABHxVwBJ7bR1ECvPzyYinsnL+sTIt+f164CpkoYBr6Ac8VdzfUQsBZZKehLYmpIw+ZOIeB54XtJPc2xDgb2BqyrjXL+dAZuZmZkNdJ5g9z/1b53+lVUTEntCo8TI84H/jIhr87i/yZU6SyvX9SmT9dYB/hwRI7szKCc5mpmZWSfwFpH+pz4B8R5gWG7HQNImktaleVpkzS3AipM+JL2qxfObAr/P62PbGOsM4F2SNshV63fCilX2RyUdnv0qY9rNzMzMOp4n2P1PfQLi+ZS48/MlzaNMmjcAfgHs3OwlR+DLwKskLcy6B7ToezJlW8ds4OlWA81tK9cC84EbgAVA7U3Fo4APZ7+LgPe0as/MzMysE/gc7H5kTRMQ1yZJQyNiiaSNKC9M/nNE3LcmbTnJ0czMzAYKn4NtvekSSTtTVtUvX9PJtZmZmVmn8Ap2PyZpJLBNRPysxXNrnLQoaTKwJCL+Y40H2kOc5DgwOcnRzMwGI69gD1wjKWdLN51gO2nRzMzMrP/wS46pkqI4RdLDmaZ4kKQZkn4taZykzSX9OFMR75G0e9adLOnyTCx8XNJ7JX01ExRvlLRePjda0h2ZbnhTnjdNJiSekymND0vaT9IrqEtrzH4mVca8MMfdcuxt/gYnSLpB0oZ5PSvTHq/OPdZkHxfl939E0vhMeXxA0pRKWxdlQuMiSV/ssX9RZmZmZv2cJ9irehPwNUpq4QjKMXn7UpIIPwN8EZiTqYifAb5bqbs98Dbg3ZSAmF9ExG7Ac8AhOck+H5iQ6YaXAmdV6q8bEeOATwBnRsQLtJfW2O7Ym5L0Mcoxe4dGxHPANRExNtMeHwA+XHn8VcBewGmUU0S+DuwC7JbbWgA+m//bZHfgrbW/jHTRr6PSzczMrKN4i8iqHo2IBQCSFgG3RkRIWgAMB95IiQYnIm6T9GpJr8y6N0TEi/nsEODGLK/VfTOwK3BLphsOARZX+r4m/zk7n+/psTdzDPBbyuT6xSzbVdKXgc2AocBNled/Wmn7ibp+hwNzgSMk/TPlv7FhwM6U4/xWERGXUGLVWX/YDn4hwMzMzAY8T7BXVU0rXF75vJzyW724Wo26uhGxXFI1KbFWV8CiiNirWX2apyS+xKr/16Ga8Nhq7M0soOz3fh0r49GnUCbc8yQdB4zvoq9qPyv6krQtZeV8bEQ8k1tHWqZROsnRzMzMOoG3iHTPdEqAChkl/nSmFrbjIWDLWkqjpPUk7dKiTn1a42PAqKw/Cti27ZE3Nwf4CHCtpG2ybBNgcW5tOaqb7b0SeBb4i6StgX/ooXGamZmZ9XueYHfPZGC0pPnA2bQXJw5A7qmeAJyT6YZzgb1bVKtPa7wa2Dy3YnwMeLj7X6Hh+O6irDpfL2kL4PPAvZQ49Ae72dY8yqT9QeAH2YaZmZnZoOBzsK3fcJKjmZmZDRTNzsH2CraZmZmZWQ/yS479kKThwHURsWuLZ/aOiB/k5zHAMRHx8XwpcUxEfEzSicDfKKeWfBH4K+VlSWgz7bGLvle0v6bj78qC3/+F4Wdc393h2FrmJEczM7NVeYI9cA2nnHX9A4CI+BWw2v6KiLi4di3pWGBSPmtmZmZmvcBbRPqIpLMlnVz5PFnS6ZLOzUTGBfkiY3294ZkQeV/+qb0YeTawX74AeVomKl7XRf3JkiZJmkCJXb8i6xwi6ceV594uaVqT8R+fKZEzgX0q5VtLmpaJj/Mq4xsi6duZ5HizpA27+5uZmZmZDUSeYPedqcARlc9HAE9Szp/eAzgIOLcWn17xJPD2iBgFTATOy/IzgOmZ8vj1Vp1HxI8oK9xHRcRI4GfACElb5iPHU9IlV5Nj+iJlYr0vJTSm5jzgjkx8HAUsyvIdgG9GxC7An8mAni7adpKjmZmZdRRPsPtIRMwBtpK0jaQ9gGcok+srI2JZRDwB3AGMrau6HvDtTE28ilUnty9nPAF8Dzha0maU6PMbGjy+J3B7RDyVxw1WY9vfBlyUbS6LiNos+dGImJvXDdMpI+KSiBgTEWOGbLTpy/pOZmZmZv2B92D3rasoZ2G/hjJJbSco5jTgCcoq9zrA8z04nsuAn2abV0XESy2e745qwuMyoOUWESc5mpmZWSfwCnbfmgq8nzLJvoqSDDlR0pDcqrE/MLOuzqbA4ohYDnyQchoIrJ7y2I5V6kTEH4A/AJ+jTLYbuRd4q6RXZ7Lj4ZV7twIfBcjv4WVoMzMzG9Q8we5DEbGIMsH9fUQsBqYB84F5wG3AJyPij3XVLgSOzfTHEZQIcrLesnyx8LQ2hzAFuDhfcqytKF8B/DYiHmgy7sWUFMu7KamM1WdPBQ7ILSyz6aEtLGZmZmYDlZMcBzlJFwBzIuK/1/ZYnORoZmZmA0WzJEfvwR7EJM2mrIj/69oei5mZmVmn8AR7EIuI0fVlku4F1q8r/mBELOjt8TjJcWBykqOZmdmqvAe7n8lgmYVtPHNk5fMYSefl9XG57QNJJ0o6plK+Tav+I2LPPFt7xR/K3uuuxnF7RrTXl0+WNKlVX2ZmZmadyCvYA9NwuhmTDhwHLKScGmJmZmZmvcQr2H1goMek5zNfz9jzWyvpjwAfzDYXShpXKd85V7gfkfTxJu06ydHMzMw6iifYfWPAxqSnjYFfZez5HcCZlXsbZZsn1bUxAjgYGAecmedndzU2JzmamZlZR/EWkT4QEXMkbZV7oLekLiYdeEJSLSZ9fqXqesAFkkZS0hB37KHxhKRaTPpllJj0Y5pUWc7KePTvA9dU7l2Zbd4p6ZUZuw5wfUQsBZZKehLYGvhds3E5ydHMzMw6gSfYfaeTYtKjwXX1c31Uuv9bMzMzs0HBW0T6zkCNSYfy38mEvD4SuKtybyKApH2Bv0SEN1KbmZnZoOZVxT4SEYskrYhJz5cK96LEpAcZky5peKXahcDVedTejXQRk06JP5/TxhCmUGLSnwP2iojnKDHpWzaLSU/PAuMkfY6yL7z6QubzkuZQtrN8qI1xmJmZmXU0R6UPYv0pJh0clW5mZmYDh6PSbTWOSTczMzPrHZ5gN5EnYhwZERf2UvtLImJob7TdSouY9J2BhygvJ/ZJTDo4Kn0gcky6mZnZ6vySY3ObUc53XoWkAfkXk1bjrsWkU15+PCDP2e6TybWZmZlZp/AEu7mzge0zqXBWpipeC9wPIOloSTPz/rckDcnyJZLOkjRP0j2Sts7ybSXdncmNX27WcaYz3inpekkPSbpY0jp56siUSgLkafn8CTnGeZKulrRRlk/JuvcCX23Q16sl3ZxJjd8BVLn3L9nXQkmfyLLTa+mMmfB4W16/TdIVzX6DLvp2kqOZmZl1FE+wmzsD+E2u6p4OjAJOjYgdJe1EOU1jn7y/DDgq620M/202kgAAIABJREFU3BMRewB3Aidk+TeAiyJiN2BxG/2PA06hbNnYHngvJaDmtRGxa7ZTO2LvmogYm30+AHy40s7rgL0j4l8a9HMmcFcmNU4D3gAgaTQl5XFP4O+AEyS9hXLE4H5ZdwwwNJMa98vv2+w3WIWTHM3MzKzTeILdPTMj4tG8PhAYDcySNDc/b5f3XgCuy+vZwPC83odMPgS+12Z/j2Ta45XAvsAjwHaSzpf098Bf89ldc4V9AWWiv0ulnauyjUb2pyQ0EhHXU5Imyf6mRcSzEbGEkuC4X36n0ZJeSQmUuZsy0d6PMvlu9huYmZmZdbQBuZd4LXq2ci3g8oj4dBfPvRgrzz+sTzHszrmIq6UkRsQzkvYADgZOBI6gnD89BTg0IuZJOg4Y32DcL1tEvCjpUeA44JeUc7kPAN5EWT2H5r9BlxyVbmZmZp3AK9jNNUtMvBWYIGkrAEmbS3pji/ZmUNIcYeV2kmbG5b7tdSjbUe6StAWwTkRcTUlhHJXPbgIszq0a7bRddScloRFJ/wC8KsunA4dK2kjSxsBhrFyhng5MyrrTKZP9OZVJtZmZmdmg5Al2ExHxJ2CGpIXAuXX37qdMcG+WNB+4BRjWoslTgZNzG8dr2xjCLOACyqrwo5T90a8Fbs9tKd8HaivonwfupUziH2yj7aovAvtLWkTZ5/2/ABFxH2VlfGa2/Z2IqKVGTqd837sj4gngeVZOvs3MzMwGLSc59lOSxgOTIuKda3ssfcVJjmZmZjZQNEty9Aq2mZmZmVkP8kuOa5mk3Vj9RJGlEbEncPvLbHsksE1E/Cw/H0/ZplI1IyJOfjn9tDmWX0bE3s2ecZLjwOIURzMzs655gr2WZVLiyF5qfiTl+LyfZV+XsfLc7B4nad2IeKmre60m12ZmZmadwltEeoik4ZIezOTEhyVdIekgSTMk/VrSuDxp5MeS5me64e5Zd7Kky/Mc68clvVfSVzOp8cY8GQRJoyXdIWm2pJskDcvy2yWdk6mSD0vaT9IrgC8BEzNpcmL2M6ky5oU57pZjb/K9J0v6nqQZwPck7aKV6ZbzJe2Qzy1pUN9JjmZmZtZRPMHuWW8CvgaMyD9HUsJaJgGfoZzWMScids/P363U3R54G/Buyukgv8ikxueAQ3KSfT4wISJGA5cCZ1XqrxsR44BPAGdGxAvAF4CpETEyIqa+zLE3szNwUER8gHJc3zcy3XIM8LtmFZ3kaGZmZp3GW0R61qO55YM88u7WiIg8lm848EbgfQARcZukV2caIsANGeCyABgC3JjltbpvBnYFbpFEPlONW78m/7mmqYmtxt7MtRHxXF7fDXxW0uso8e2/XoOxmJmZmQ1YnmD3rKWV6+WVz8spv/WLrepGxHJJ1RTEWl0BiyJirxZ9N0tNfIlV/6/FBt0YezMrkiIj4geS7gUOAX4m6SMRcVuL+oCTHM3MzKwzeItI35pOpizmOddPR8Rf26z7ELClpL2y/nqSdmlRpz6J8jEy+VHSKGDbtkfeJknbAY9ExHnAT4Dde7oPMzMzs/7ME+y+NRkYncmPZwPHtlsx91RPAM6RNA+YC7Q6meMXwM61lxyBq4HNcwvIx4CHu/8VWjoCWJhJk7uy6j5zMzMzs47nJEfrN5zkaGZmZgOFkxzNzMzMzPqIX3LsBkmbAUdGxIW91P6SiBjaG22/XJUUyFdTXnx8hjZTICXdDkyKiKbL005yHBic4GhmZtacJ9jdsxlwErDKBLtZgmFfUjm/TxGxvKfb7u0USDMzM7NO4S0i3XM2sH2+NDgrkxevBe4HkHR0JcXwW5KGZPkSSWdJmpcJjltn+baS7s7Exi8361jSUEm3Srovn39Plg+X9JCk7wILgddL+kA+s1DSOZU2GpV3Ob4G41iRBtlVgmSWbyjph5IekDQN2LBJe05yNDMzs47iCXb3nAH8JlMKT6cceXdqROwoaSdgIrBP3l9GHskHbAzcExF7AHcCJ2T5N4CLMrGxGhrTleeBwyJiFHAA8LVcsQbYAbgwInahnLV9DiUVciQwVtKhkrbpqrzF+NqxSoJkln0U+FtE7JRloxtVdpKjmZmZdRpPsF+emRHxaF4fSJlIzsoj6g4Etst7LwDX5XU1aXEf4Mq8/l6LvgT8ex7x93PgtUBtpfnxiLgnr8cCt0fEU7lt5Qpg/yblzcbXjq4SJPenxL0TEfOB+d1oz8zMzGxA8x7sl+fZyrWAyyPi0108V01mrE9abPecxKOALYHRGan+GCuTGJ9tWKs9zcbXSjsJkm1xkqOZmZl1Aq9gd099MmLVrcAESVsBSNpc0htbtDcDeH9eH9XsQWBT4MmcXB8ANGp7JvBWSVvkHvAPAHc0Ke8NdwJHAkjaFac5mpmZ2SDiFexuiIg/SZohaSHwHPBE5d79kj4H3CxpHcpe6JOBx5s0eSrwA0mfosSKN3MF8FNJC4BfAQ82GONiSWdQUhwFXB8RPwFoVN4LLgIuk/QA8ABl+4iZmZnZoOAkR+s3nORoZmZmA4WTHM3MzMzM+oi3iPQzknZj9RNFlkbEnn2ZJCnps8DhlduvABZExMQGdbcBzouICWvav5Mc+y+nN5qZmbXPE+x+JiIWUM6p7kqfJUlGxFnAWZU+xgOTuno2+/8DsMaTazMzM7NO4Qn2wLIiSZLyEuXzwDPACGBHSUcDH6esNt8LnBQRyyQtoYTavJPycuZ7IuIJSdsCPwCG0voly7OBnbLvy7Pf92bdIZKOBa6LiF0lnQbsFhEfyhX5K4FxEfG3nvspzMzMzPon78EeWNZmkuQZwPSIGBkRX8+yUcCEiHhr3bPfAN4k6TDgMuAjjSbXjko3MzOzTuMJ9sDWl0mSXbklIv5ffWFELAeOyzbviIgZjRpwVLqZmZl1Gm8RGdj6MkmyVf/1dgCWANu025iTHM3MzKwTeAV7YFmbSZLN+l6FpE2B84D9gVdL8suPZmZmNmh4gj2ARMSfgFqS5Ll19+4HakmS84FbgGEtmjwVODnTIV/b4tn5wDJJ8/Ilxma+DnwzIh4GPgycXZv4m5mZmXU6Jzlav+EkRzMzMxsonORoZmZmZtZH/JKjraJZkmRv9+0kx/7LSY5mZmbt8wp2PyRpeO6zbvXMkZXPYySdl9fHSbogr0+UdEylvOmpHhGxIM+6rv7ZM8Nq2h3/7ZK6/F8mZmZmZp3OK9gD13DgSEoSIxHxK2C1DcwRcXHl43HAQuAPvT88MzMzs8HJK9h9RNLZkk6ufJ4s6XRJ50paKGmBpIld1Bsuabqk+/LP3nnrbGA/SXMlnSZpvKTruqg/WdKkPCpvDHBF1jlE0o8rz71d0rQ2vscWku6WdEh+/lSOfZ6ksyuPHi5ppqSHJe3XpD0nOZqZmVlH8QS770wFjqh8PgJ4EhgJ7AEcBJwrqf5ovSeBt0fEKEoU+nlZ3lV0eUMR8SPKCvdRGaX+M2CEpC3zkeOBS5u1IWlr4HrgCxFxvaR/AN4D7Jkx7F+tPL5uRIwDPgGc2WRcTnI0MzOzjuItIn0kIuZI2ir3QG8JPEOZXF8ZEcuAJyTdAYylnDldsx5wgaSRlBTGHXtoPCHpe8DRki4D9gKOaVJlPUqYzckRcUeWHQRcFhF/yzarsenX5D+r0exNOcnRzMzMOoEn2H3rKmAC8BrKiva2bdQ5DXiCssq9DvB8D47nMuCn2eZVEfFSk2dfokyWDwbuaPJczdL8Z300u5mZmVlH8xaRvjWVEk0+gTLZng5MlDQkt2rsD8ysq7MpsDgilgMfBIZkedvR5RWr1ImIP1BeePwcZbLdTAAfomwr+VSW3QIcL2kjKPHs3RyPmZmZWcfxBLsPRcQiygT39xGxGJhG2Q4yD7gN+GRE/LGu2oXAsZLmASOAZ7O8O9HlNVOAi/Mlxw2z7ArgtxHxQBvjXwZ8AHibpJMi4kbgWuBXkuYCk9och5mZmVnHclT6IJfnZc+JiP9e22NxVLqZmZkNFM2i0r03dhCTNJuyIv6va3ss4CTHtcUpjWZmZj1r0G8RkbSZpJN6sf22ExD7WkSMjoj9I6L2QiKS7pW0LLeR1P7stjbHaWZmZjaQeAUbNgNOoux1XkHSui1O1egTkkTZyrO8L/qrxaLnWdlmZmZm1k2DfgWbkoi4fa7UzsrUxGuB+wEkHZ2JhHMlfUvSkCxfIumsfMnwngxhQdK2mXS4QNKXm3UsaaikWzOhcYGk92T5cEkPSfouJdr89ZI+kM8slHROpY1G5V2Or8E4uhyzitWSJiV9U9K783qapEvz+kPZ53BJD0j6tqRFkm6uvFRZ37eTHM3MzKyjeIJdEhF/kyu2pwOjgFMjYkdJO1HSE/fJ+8uAo7LexsA9mWB4J3BCln8DuCgidgMWt+j7eeCwTGk8APharlgD7ABcGBG7AC8C5wBvo4TTjJV0aIbWrFbeYnxdaTTm99J10uR0oBZ//lpg57zeL/uqjf+bOf4/A+/rqmMnOZqZmVmn8QR7dTMj4tG8PhAYDczKY+gOBLbLey8A1+V1Na1wH+DKvP5ei74E/Luk+cDPKZPV2krz4xFxT16PBW6PiKdy28oVlDOzG5U3G19XGo15XzJpMiKeoATMjCUn2JJ2pqz0P5ET772AX2bdRyNibpv9m5mZmXUM78Fe3bOVawGXR8Snu3juxVh5xmF9WmG7Zx8eRYlNHx0RL0p6DNigi3GsiWbj60rb5zVGxO8lbQb8PWXFenPgCGBJRPyfpFezMsmx1n+XW0SqHJVuZmZmncAr2M0TEW8FJkjaCkpSoaQ3tmhvBiWtEVZuJ2lkU+DJnFwfADRqeybwVklb5B7wD1BWkxuVd1ejMTdLmrwH+ARlgj2dEjIzfQ36NjMzM+sog36CHRF/AmZIWgicW3fvfkqM+M25jeMWYFiLJk8FTpa0gLLlo5krgDH57DHAgw3GuJiyV/wXlNTH2RHxk0blLfrszpibJU1OB9aNiP8PuI+yiu0JtpmZmQ16TnK0fsNJjmZmZjZQNEtyHPQr2GZmZmZmPaljXnLMl+6OjIgLWz68Zu0viYihDe5tA5wXERMa3N+NcpLGQ5QX/gCWZqjLu4GdI+Ls3hxj3v8scHhd8VURcdbL7bsnOCq97zge3czMrPd0zASbtZjIGBF/ALqcXOf9BZL+ABwQEU/X3bsWuLY3x1fp6yygX0ymzczMzDpVJ20RWZuJjMPzJUnyxI3/yPTD+ZJOqTx6SiW1cUQ+f5ykC/J6iqSLchyPSBov6dJMRZxS6a/L9MbK/S1y7IdkG9dV7l0g6bgm32WspF/m7zFT0iaSNpB0WfY5J088qY39Gkk3Svq1pK9m+YmSzq20ueI7dtGfkxzNzMyso3TSBHttJjJW/TMlVGVkROxOOSmk5ulMbbyIcqxdV15FCWw5jbKy/XVgF2A3SSNbpDeSf0G4HvhCRHRrv4WkVwBTKb9bLb3xOeBkIPK3+ABwuaTaed0jKb/tbpQj/V4PXA0cVml6IvDDrvp0kqOZmZl1mk6aYNfry0TGqoOAb9W2pUTE/6vcu6aLfur9NANiFgBPRMSCiFgOLMo6zdIb16Oc3f3JiLilG2OueTOwOCJm5dj/mn3sC3w/yx4EHgd2zDq3RsRfIuJ5yv8teGNEPAU8IunvMnRmBOWsbTMzM7OO10l7sOv1ZSJju2rphs2SFWvPLGfVNMTlWefFJu2/RJm8H8zKwJmXWPUvUhvUV3qZ6hMba9/rh5R0xweBaZXfuCEnOZqZmVkn6KQV7LWZyFh1C/ARSevW+upG3XY0S28M4EPACEmfyrLHgZ0lrZ8nrRzYpO2HgGGSxubYN8nvMZ38DSTtCLwhn21mGvCeHF+X20PMzMzMOlHHTLDXciJj1XeA/wXmS5oHHNmNui21Sm+MiGWUSe3bJJ0UEb8F/gdYmP+c06TtFyj7pc/Psd9CWfG+EFgnf4upwHERsbRRO9nWM8ADlC0jM5s9a2ZmZtZJnORo/YaTHM3MzGygcJKjmZmZmVkf6eSXHJtak+THTGSsP1FkaUTs2cWzTVMVe1uedT0mIj7W4P40YNu64u8AO0bEx3t4LJOBJRHxH82ec5Jj73J6o5mZWd8YtBNs1iD5MSIWUM59HvAi4rDWT5mZmZlZdw3mLSJrM/lxvKQ7JP0kExvPlnRU9rdA0vb53Lsk3ZvpiT+v9DW0kqw4X9L7svx4SQ9Lmkk5x7vZGA7PJMh5ku6sjOu6vN5S0i2SFkn6jqTH8+SS4SrJkt/OezdL2jDrnJC/5TxJV0vaqNW/BCc5mpmZWacZzBPstZ38uAdwIrAT8EHK1oxxlG0atXj1u4C/i4i3UI66+2SWfx74S0TslmmRt0kaBnyRMrHeF9i5Rf9fAA7O7/HuLu6fCdwWEbsAP6IczVezA/DNvPdn4H1Zfk1EjM02HwA+3OpHcJKjmZmZdZrBPMGu19fJj7MiYnEed/cb4OYsX1Bp83XATXk83umUyHQoaZHfrDWUR+LtycqExxcox+k1MwOYIukEYEgX9/clz6+OiBuBZyr3Ho2IuXld/Q12zf8TsIDyF5JdMDMzMxtkBvMe7Hp9nfxYn9JYTXCstXk+8J8Rca2k8cDkbrTfVEScKGlP4BBgtqTR3ahen964YV5PAQ6NiHn5kuX47ozJSY5mZmbWCQbzCnZ/SX5sZlPg93l9bKX8FuDk2gdJrwLupSQ8vlrSesDhzRqWtH1E3BsRXwCeAl5f98gMStQ5kt4BvKqN8W4CLM7+e+o3MDMzMxtQBu0Eux8lPzYzGbhK0mzg6Ur5l4FX1V5SBA7IhMfJwN2UyfEDLdo+N1+SXAj8kpIKWfVF4B15/3Dgj5S/lDTzecpEfwbwYItnzczMzDqSkxytS5LWB5ZFxEuS9qK8wNmrRxQ6ydHMzMwGimZJjt6DbY28AfgfSetQXuw8ocXzZmZmZoYn2D2mq2TI7iQ/ttH+GiVDSvosK/djv4byUuKFEXFWs3oR8WvgLd3t7+VwkmPvcpKjmZlZ3/AEu+eslgwZEQskjWmUDNkXciJ9FrQfWW5mZmZma27QvuTYC9ZmMuRQSbdKui+ff0/l3mcz3fEu4M1Ztm6OcXx+/oqkhivakh6T9MVK+yOyfGNJl+b3mlPrV9JGkv5H0v2SpmUaZZd7lMzMzMw6jSfYPWdtJkM+DxwWEaOAA4CvqRhNOTpwJPCPwFiAXFE/DrhI0kHA31NODWnm6Wz/ImBSln2WkvY4Lvs9V9LGlJX8ZyJiZ8rJIg3P2HZUupmZmXUaT7B7T18mQwr49zxS8OeUYwK3BvYDpkXE3yLir8C1tQoRsSjbvQ74UKY/NnNNF2N8B3BGfqfbgQ0oL0dWUyAXAvMbNeqodDMzM+s03oPde/oyGfIoYEtgdES8KOkxymS3ld2APwNbtfFsLb2xOkYB74uIh6oPSmpnzKsPxkmOZmZm1gG8gt1z1mYy5KbAkzm5PgCotX0ncKikDSVtAryrVkHSe4HNgf2B8/MUlO66CThFOaOWVDt1pJoCuTNlIm9mZmY2KHiC3UPWcjLkFcCYfPYYMkUxIu4DplJSGm8AZgFI2oLyUuY/RcTDwAWUPd/d9W/AesB8SYvyM5STVLaUdD8ldXIR4A3WZmZmNig4ydF6XJ6Qsl5EPC9pe8q+8De32uftJEczMzMbKJzkaH1tI+AXktaj7NM+qY2XKM3MzMw6gifYA0hPJkM2aH8asG1d8aci4qbutBMR/wd0+9xrJzmuGSc0mpmZ9S+eYA8AkoYD10XErpQzrRs9s3dE/CA/jwH+//buPO7qss7/+OsNkril4ZbLJGoqLgghYC6YO5WOy0Qyaio22ZhL5gwm/caMZmoGs4lScilLzNAYNIskd0URF1CRTdRSmVxI0xoVFWT5/P64ruP95XDuc5+bez/3+/l4nAff812u73Wu+yjXfXF9r/epEfFVSaOAwRFxjqQzgXcj4hd5/50R8QpARBzfjDrVHN0uaWKu/021lm9mZmbWVbmDXT/6AicBNwBExGPAWhOaI+KqwttRwALglbavnpmZmVn34FVEOoikcZLOLrwfK+kCSZdKWpAjyUdWuK5vjmF/Ir/2z4fGAcNyFPv5kg6WdGuF68dKGi1pBGkax6R8zVGSflM474g8ZaSpz7FFjnQ/Kr+/MNd9rqRxNVzvJEczMzOrK+5gd5zJ5LWisxOA10hTQAYAh5Oix8uX83sNOCLHlo8ELsv7xwAzImJgRIxv6uZ5usZjwMk5vv33QD9JW+ZTTgd+Xq0MSVsD04CLI2KapM8AxwL75uj379VQDyc5mpmZWV3xFJEOEhFzJG0laVtSCuPfSJ3rGyNiFfCqpPuBIawZNd4LmCBpIClVcddWqk9Iuh74gqRrgf1Ia2o3phcpQOfsiLg/7zscuDYi3s1l/rU5dXCSo5mZmdUDd7A71hRgBPBR0oh2+QoelZwPvEoa5e4BLGvF+lwL/C6XOSUiVlY5dyXwODAcuL/KeWZmZmbdiqeIdKzJpDj0EaTO9gxgpKSeearGQcCssms2BZZExGrgFKBn3l8tqr0xa1yTVxN5hZQ6eW0T1wbwRdK0kgvzvruA0yVtCCkSvpn1MTMzM+vy3MHuQBGxkNTBfTkilgC3kKaDzAXuBb4eEX8uu+wK4DRJc4F+wDt5/zxgVX648PwaqzARuCo/5LhB3jcJeDEiFtVQ/1XAicChks6KiNuBqcBjkp4ERtdYDzMzM7O64ah0W4OkCcCciPhZe9/bUelmZmbWVTgq3Woi6XHSiPi/dsT9neS4bpzkaGZm1rl0iikikjaTdFYblr90Ha4ZLOmyps9sVpkT8/rTjR2/RtIeVY6PlVRx2oWkh1pav4jYB3ge+PtCuY/mKSTFV/9q5VSrp5mZmVm96ywj2JsBZ5HmF39A0npNrGTRZhpLQmzje36pBdfu3/RZ61Tuvm1RrpmZmVm96hQj2KQUwp3z6OjsnFQ4FXgKQNIXJM3Kx6+W1DPvXyrpu/nBvkdy8AmSdszpgvMlfafajSX9qpRCmN9PlDSimIQo6VOF0ds5kjYpT0qUNEHSqLx9cf4cCyT9RJJqaQRJ0yUNztufzkmNcyXdUzhtj3ze85K+Wrh2af7zYEn3S/ptPmecpJNz+82XtHM+r6+keyXNk3SPpI9VqM9/5PboKWmxpC3y/sGSpjfxcQbkn8EfJJ1R5TM7ydHMzMzqSmfpYI8BnsuJghcAg4DzImJXSbuTEgsPyMdXASfn6zYCHsmpgQ8ApY7cj4ArI6I/sKSJe3+QqCjpQ8BhpHTCotGkQJWBwDDgvSbKnBARQyJiL2AD4Ogmzl9DXqLvp8Dn8mf7fOFwP9La00OBb0nqVaGIAcCZwO6kpfx2jYihwDXAufmcy4HrImJv0soha0yHkXQpKQDn9LxaSHPtDRxKCqy5OAfqrMVJjmZmZlZvOksHu9ysiHghbx8G7APMzku/HQbslI+9D5RGkR8H+ubtA4Ab8/b1TdzrNuAQSesDnwEeiIjyDvRM4Ad5xHizGqatHJLnLs8ndTL3bOL8cp/M9XgB1kpEnBYRyyPidVJs+tYVrp8dEUsiYjnwHHBn3j+fhjbaD7ghb18PHFi4/pvAphFxZqz7MjO/jYj3cj3vI/1CYGZmZlb3Ossc7HLvFLZFGmn9RoXzVhQ6gKtY8/PU1DGMiGV5usNw0kj5ryqcM07SNOCzwExJw0lJhsVfUHoDSOpNmks+OCJelDS2dKyVLC9sl3/mSuesLrxf3cj55WYD+0jqU+jcFz9vLZ+nvP2b/Hk4Kt3MzMzqQWcZwa6WQngPMELSVpDSASXt0ER5M0kJidAwnaSaycDppOkft5cflLRzRMyPiEtInc9+wP+S5kOvL2kz0sg6NHQ+X5e0MSmlsbkeAQ6StGO+f1skIj7Emm00o3DsdtK8+GmSSj+XxaR/SQD4XA3lHyupt6TNgYNJ7WZmZmZW9zrFCHZEvCFppqQFpPnNrxaOPSXpIuBOST2AFcDZpA5uY84DblCK8P5tDVW4kzRN4rcR8X6F41+TdAhpBHghcFtELJf0P8AC4AVgTq7v/0n6ad7/Z9ahYxkRf5H0ZeDX+TO/BhzR3HKacC5wraQLgL+QfsEo1mFK7lxPlfRZ4NvAzyT9BzC9hvLnkaaGbAH8R45hNzMzM6t7TnK0TsNJjmZmZtZVqEqSY2eZImJmZmZmVhc6xRSR9pDTB8tXFFnemkEqeS72SRFxRRPn3QLsWLb7woi4o4nrlkbExi2s41hgaUR8vyXl5LJOJ03HKZoZEWevS3mOSm8eR6SbmZl1Tt2mgx0R84GBbXybmhIpI+L4Nq5Hu4iIa4FrO7oeZmZmZp2Jp4i0ro5MpNw4JzI+kc8/tnDs3yQ9K+lBYLe8b71cx4Pz+/+S9N0q5S+W9L1c9ixJH8/7KyZCSvq8UpLlXEkPVCnXSY5mZmZWV9zBbl0dmUi5DDg+IgYBhwD/rWQf0nJ8A0nreA8ByCPqo4ArJR0OfJq0Ukg1b+a6TAB+mPc1lgh5MTA8f6ZjGivQSY5mZmZWb9zBblvtmUgp4D8lzQPuBrYjpTwOA26JiHcj4i1gaumCiFiYy70V+GIjSxQW3Vj4c7+83Vgi5ExgoqQzgJ5NlGtmZmZWN7rNHOwO0m6JlKTR8C2BfSJihaTF1Ja42B/4P2CrGs6NRrbXPjHiTEn7AkcBj0vaJyLeqFoRJzmamZlZHfAIduvqyETKTYHXcuf6EKBU9gPAcZI2yMExf1+6QNI/AH2Ag4DL8yoo1Yws/Plw3q6YCJnTLx+NiItJQTZ/10TZZmZmZnXBI9itqIMTKScBv5M0H3gMeDrf9wlJk4G5pETI2QCStiA9lHlYRLwoaQJpzvdpVe7xkTwFZTlwYt7XWCLkpZJ2IY3c35Pvb2ZmZlb3nORoNclTTgZHxOttdQ8nOZqZmVlX4SRHMzMzM7N24ikinZSkvsCtEbHD1jkLAAAgAElEQVRX2f5iIuWHgA2BVyNiX0mDgVMj4quSRpFGnM+RdCbwbkT8Iu+/MyJeaeS+jaVM9pW0FGhRkmQ1TnKszsmNZmZmXYM72F1MMZEyh8SMjoij87HHSPOvy6+5qvB2FLAAqNjBrpeUSTMzM7OO4iki7UjSOElnF96PlXSBpEtz6uF8SSMrXNc3p0I+kV/750PjgGE5GfJ8SQdLurXC9WMljZY0AhgMTMrXHCXpN4Xzjsgj2E19ji1ywuRR+f2Fue5zJY3L+z4u6e687wlJOzdSlpMczczMrK64g92+JgMnFN6fQFrZYyAwADictPrGNmXXvQYckVMaR9KQljgGmBERAyNifFM3j4ibSCPcJ+c0yd8D/SRtmU85Hfh5tTJyjPs04OKImCbpM8CxwL45tfF7+dRJwI/zvv1pJInSSY5mZmZWb9zBbkcRMQfYStK2kgYAfyN1rm+MiFUR8SpwPznOvKAX8NO8BN8UYI9Wqk+Q5nN/Ia+BvR9wW5VLepGW3Pt6RNyV9x0OXBsR7+Yy/5rX294uIm7J+5aVjpuZmZnVO8/Bbn9TgBHAR0kj2uUPFFZyPmlN7QGkX4qWtWJ9rgV+l8ucEhErq5y7khTlPpz0i0CrcpKjmZmZ1QOPYLe/yaTkwxGkzvYMYKSknnmqxkHArLJrNgWWRMRq4BSgZ95fLTmyMWtck1cTeQW4iNTZriaAL5KmlVyY990FnC5pQ0gJlRHxNvCSpOPyvvVLx83MzMzqnTvY7SwiFpI6uC9HxBLgFmAeKenwXtL0iz+XXXYFcJqkuUA/4J28fx6wKj9IeH6NVZgIXJUfctwg75sEvBgRi2qo/ypSiuOhks6KiNuBqcBjkp4ERudTTwG+mpMfHyKN2JuZmZnVPSc5GjkmfU5E/Kwj6+EkRzMzM+sqqiU5eg52NyfpcdKI+L92dF3MzMzM6oE72J1AY6mNFc7ZPyJuyO9bnNoIEBH7VLjXo6T1sucXdp8SEfMlTSeF27T6ULOTHBvnFEczM7Ouwx3srqMvcBJwA7ROamNjcuz60rxWtpmZmZk1gx9ybCP1kNooabykhZLuKYTRAJySy1wgaWjhvtfluv+vpH+Q9L38OW+X1KtZDWhmZmbWRbmD3Xa6emrjRsBjEbEnac3rbxWObZjLPKusjJ2BQ4FjgF8C90VEf+A9oOIcB0elm5mZWb1xB7uN1EFq42rSLwmQOssHFo7dmMt8APhwLg/gtohYQZq73RO4Pe+fT5riUqlejko3MzOzuuI52G2rK6c2lotGtovvlwNExGpJK6JhDcjV1PBdc5KjmZmZ1QOPYLetrpza2CPXG9LDlQ8Wjo0EkHQg8GZEeG6HmZmZWeYR7DYUEQslfZDamB8q3I+U2hjk1Ma8BF/JFcDNkk4lTbFYK7WRlMY4p4YqTCSlNr4H7BcR75FSG7esIbXxHWCopItI88KLD2QukzSHNJ3lizXUw8zMzKzbcJJjN9NZUhsrcZKjmZmZdRVOcjTAqY1mZmZm7cEd7DJ5RYyTIuKKNip/aURs3MixbYHLImJEpeP5nMWk1MbXy/YfA+wREeMau7ZKauP6Zbt3jYgNG/8UbaO7Jjk6pdHMzKy+uIO9ts1I6zuv0cGWtF4zV91otvwQYqOd6yaunQpMXYfr9i3fJ2nputTBzMzMzLyKSCXjgJ1zUuHsnEw4FXgKQNIXJM3Kx6+W1DPvXyrpu5LmSnpE0tZ5/46SHs6Jht+pduOc4rggb/eU9P2cljhP0rmFU8/NKY/zJfXL54/K86uRNFHSlbkez+fUx59LWiRpYuF+J+YyFki6pEJ9tsh1P6o8OVLSBEmjqnyWi3P7LZD0E0lqot3NzMzM6oI72GsbAzyXkwovAAYB50XErpJ2J62mcUA+vgo4OV+3EfBIRAwAHgDOyPt/BFyZEw2XNKMeXyaFswyMiL1Jq3+UvJ6THq8ERjdy/UdIK5acTxrZHg/sCfSXNDBPR7mElLw4EBgi6bjSxfkXhGnAxRGxLvM2JkTEkIjYC9gAOLrSSU5yNDMzs3rjDnbTZkXEC3n7MGAfYLakJ/P7nfKx94HSCO/jNCQXHkBOPiQlKdbqcODq0rSUiPhr4divK9yn3O9y0Mt84NWImJ/X1l6YrxkCTI+Iv+R7TCKtyw1p+b17SMsI3tWMOhcdIunRnEh5KKlzvxYnOZqZmVm98Rzspr1T2BZwXUR8o8J5xeTCVazZtq29FuLyRu5T6ZzVhe3S+/WAFVXKX0nqvA8nxbmX9hV/Ievd2MWSepPmsA+OiBclja12fomTHM3MzKweeAR7bdUSE+8BRkjaCkBSH0k7NFHeTFKaIzRMJ6nFXcA/S1qvdK9mXFuLWcCn8jzrnsCJNHSmgxQg00/ShXnf/wJ7SFo/r7RyWJWyS53p1yVtzDo+uGlmZmbWFbmDXSYi3gBm5ocNLy079hQpZvxOSfNIneBtmijyPODsPFViu2ZU5RrgT8C8nN54UjOubVJELCHNN7+PlCz5eET8tnB8FanTfaiksyLiReB/gAX5z0aTJCPi/4Cf5nPvAGa3Zt3NzMzMOjMnOVqn4SRHMzMz6yqqJTl6BNvMzMzMrBX5IccOIKk/a68osrxS6EtnJ+kWYMey3RdGxB3NLau7JDk6udHMzKy+uYO9jloSqR4R80lrT1crv9FI9fYm6UbSMnvXRsT44rGIOL7KdQOBbSPi921cRTMzM7NOw1NE1l0pUn0NpVU/6oWkjwJDImLv8s51DQYCn22DapmZmZl1Wu5gr7sOi1TP51+Q7ztP0rfzvr6Sns5R6c9KmiTpcEkzJf1B0tB83tB8rzmSHpK0W5Vb3Qlslz/HMEln5PvOlXSzpA1zmZ/PsehzJT0g6UPAvwMj87UjG/kcTnI0MzOzuuIO9rrrsEh1SUcCuwBDSaPE+0gqpTB+HPhvoF9+nQQcSIpU/3/5nKeBYRHxCeBi4D+r3O6Y0ueMiBnAr3ME+gBgEfBP+byLgeF5/zER8X7eNzlfO7lS4U5yNDMzs3pTV9MZOlhjkeoAGwCv5WPlkepH5O0DgM/l7euBS6rc68j8Kq1FvTGpw/0n4IU8xxtJC4F7IiLyOtx98/mbAtdJ2oUUKtOrGZ9zrzzCvlm+b+lhxpnAREn/Q0OUu5mZmVm34w5262nPSHUB/xURV6+xU+rL2rHoxcj00r3+A7gvIo7P10yv8b4AE4HjImKupFHAwQARcaakfYGjgMcl7dOMMgFHpZuZmVl98BSRddeRkep3AF/MMeRI2q50rxptCryct0c14zpIn3mJpF7FekraOSIejYiLgb8Af0f1NjIzMzOrS+5gr6OOjFSPiDuBG4CH8/k30byO7PeA/5I0h+b/K8Y3gUdJvxA8Xdh/aX5AcwHwECl+/T5gj2oPOZqZmZnVG0elW6fhqHQzMzPrKhyVbmZmZmbWTvyQYyeSHzi8NSL2yu/bLVJd0nDWXrnkhWpJja2t3qPSHZFuZmbWPbiD3YnVEqneive6g4Yl98zMzMxsHXmKSBuTNE7S2YX3Y3MK46U5+XB+pQcAJY2SNKHw/lZJB+ftpfn6hZLuzsmM0yU9L+mYfE7PfE4p7fGfq9TxV5KOKryfKGmEpGmS9s775ki6OG//e0503FjSPZKeyJ/j2Hy8lCg5SdIiSTeVEh8r3NtJjmZmZlZX3MFue5OBEwrvTyCFzgwEBgCHk1bgaGqVkaKNgHsjYk/SUnjfIQXWHE+KJ4eUsPhmRAwBhgBnSNqxqTrmiPPDgGnADGCYpE2BlaQwHIBhpBTKZcDxETEIOAT4b+VkHWA34IqI2B14Czir0o2d5GhmZmb1xh3sNhYRc4CtJG0raQDwN1Ln+saIWBURrwL3kzrBtXofuD1vzwfuj4gVebtv3n8kcKqkJ0nL6m1OSnus5DbgEEnrA58BHoiI90gd7INIHetpwMZ5JHrHiHiGFHjzn3kpwrtJywtunct8MSJm5u1fkuLazczMzOqe52C3jynACOCjpNHixkaSi1ay5i9AvQvbxTTID9IaI2K1pNLPVMC5eW51VRGxTNJ0YDgwEvhVPjQbGAw8T1rLewvgDFLEO6SgmS2BfSJihaTFhXqWr//Y5HqQTnI0MzOzeuAR7PYxmZTSOILU2Z4BjMzzpLckjRLPKrtmMTBQUg9JfwcMbeY97wC+khMXkbSrpI2aqOPppOkftwNExPvAi8DngYdzvUeTpodASoR8LXeuDwGKaZUfk7Rf3j4JeLCZ9TczMzPrktzBbgcRsZCUtPhyRCwBbgHmkdIO7wW+HhF/LrtsJvAC8BRwGfBEM297Tb72iZyueDXV/8XiTuBTwN25Y10yg9SJLk0Z2T7/CTAJGJzTJE9lzWTHZ0jJlIuAjwBXNrP+ZmZmZl2Skxyt1ZWv510rJzmamZlZV+EkRzMzMzOzduKHHFtA0mbASRFxRRuVvzQiNm7F8tYpGVLSpcBngd9HxAVN3SciFgN75ZHs/SPihlrqV29Jjk5uNDMz657cwW6ZzUjrO6/RwZa0XkSs7JgqNa4FyZBfBvpExKpmXteX9IBjTR1sMzMzs3rgKSItMw7YWdKTOTFxhqSppIcLkfQFSbPy8asl9cz7l0r6rqS5kh6RtHXev6Okh3Mq4neq3VjSNpIeyGUvkDSsUHZTKY99c12fyK/9q9xnKrAx8LikkZL+XtKjOdnx7kLdP5Xr8mQ+tklun2F53/mNlO8kRzMzM6sr7mC3zBjguYgYCFwADALOi4hdJe1OWlP6gHx8FWndaEhJjI9ExADSkndn5P0/Aq6MiP7AkibufRJwRy57APBkoeymUh5fA47ICYwjSauUVBQRxwDvRcTAiJhMWm7vkxHxCdJ62V/Pp44Gzs71GQa8l9tnRr52fCPlO8nRzMzM6oqniLSuWRHxQt4+DNgHmJ3TwzcgdWwhJTHemrcfJ3WAISUmfi5vXw9cUuVes4Gf53WufxMRpQ52ecrj8rxOdTHlsRcwQVKp479rMz7j9sDkHO3+IdJSgpCWFfyBpEnAryPipYbUdDMzM7Puwx3s1vVOYVvAdRHxjQrnFZMYV7Hmz6GmdRMj4gFJBwFHARMl/SAifkFtKY/nA6+SRr57AMtquWd2OfCDiJgq6WBgbC5/nKRppIchZ0oa3owyASc5mpmZWX3wFJGWeZsUIFPJPcAISVsBSOojaYdGzi2ZSUp8hIbpJBXlsl6NiJ+SQmUG1VzrlMC4JCJWA6cAPZt57ct5+7RCfXaOiPkRcQlpdL0f1dvHzMzMrC65g90CEfEGabR2AXBp2bGngIuAOyXNA+4CtmmiyPNI6Yfzge2aOPdgYK6kOaR51D9qRtWvAE6TNJfUEX6nifOLxgJTJD0OvF7Y/7X8sOU8YAVwGymtclV+mLPiQ45mZmZm9cZJjtZpOMnRzMzMugonOZqZmZmZtZO6ecixI1MVJW0LXBYRI6pcvxgYHBGvl+0/BtgjIsY1cl3N6YstSX4s3GcP4BnSw5dNpjy2pnpKcnSKo5mZWfdVNx1sOjBVMSJeARrtXDdx7VRgapXj65q+2Nx6zAcG5l8EDin/RcDMzMzMalNPU0Q6MlWxb37QEUk9JX2/9MCfpHMLp56bkxPnS+qXzx8laULenijpylyP5yUdLOnnkhZJmli434m5jAWS1lorW9IWue5H5TJuLRybIGlUE2359Vz+LEkfz9c1J8ERSRfkn8M8Sd9u4n5mZmZmdaOeOtgdmapY9GVSoMvAiNgbmFQ49npOT7ySlHxYyUeA/UhrVU8FxgN7Av0lDczTUS4BDiWNbA+RdFzp4tzxnQZcHBHrOt/izfy5JwA/zPtqTnCUdCSwCzA013GfvGb3WhyVbmZmZvWmnjrY5RpLVXwyv98pHytPVeybtw8Abszb5XOgqzkcuLo0LSUi/lo49usK9yn3uxwUM5+0zvX8vF71wnzNEGB6RPwl32MSUOq89iKtv/31iLirGXUud2Phz/3y9vbAHXkJwQtInX5oSHD8KrBZrtOR+TUHeIK0FOAulW7kqHQzMzOrN/U0B7tcu6UqNsPyRu5T6ZzVhe3S+/VIa0w3ZiWp8z4cuL+wr/iLVO8a6hkVtpuT4CjgvyLi6hru9QEnOZqZmVk9qKcR7A5LVSxzF/DPpVhySX2acW0tZgGfyvOsewIn0tCZDuCLQD9JF+Z9/wvsIWn9vNLKYTXcY2Thz4fzdnMSHO8Avihp43zOdqW2NzMzM6t3dTOCHRFvSCqlKr4HvFo49pSkUqpiD9Io8NmkzmdjzgNuyB3V3zajKtcAuwLzJK0Afkqay9wqImKJpDHAfaSR4mkR8dvC8VWSTgSmSno7Iq6Q9D/AAuAF0rSNpnwkJzIuJ3XgoSHB8W/AvcCOef/XJB1CGmFfCNwWEcvzvPeHJQEsBb4AvNaSz25mZmbWFTjJ0ToNJzmamZlZV+EkRzMzMzOzdtLppoh0ZCJjDdfWnKrYXiRdQ3r48KlmXLOUNFd8F9LqIIvzoQsj4o4ayxgF3JlDdlpFtSRHJyOamZlZV9HpOth0YCJjU9YlVbGt6x0RX1rH645v4a1HkeZ119zBltQzIla18L5mZmZmnVpnnCLSkYmMB0t6QNI0Sc9IukpSj5zOODEnJ86XdH4+/4xcx7mSbpa0Yd4/MV/7KPC9Ru41VtLowvsFSomQfSU9LWmSUoLjTaVyGylnuqTB69IGqiGBUtLF+TMukPQTJSOAwcCk/HPYQNJhSkmO85XSJ9fP1y+WdImkJ4DPV2t/MzMzs3rQGTvYHZ3IOBQ4F9gD2Bn4B9Ko9XYRsVcu59p87q8jYki+5yLgnwrlbA/sHxH/0vwmYDfgiojYHXiLNKJfi5a0QWMJlBPyZ9wL2AA4OiJuAh4DTs4/hwAmAiPzPdYDvlIo+42IGBQRvyq/qZzkaGZmZnWmM3awy7V3IuOsiHg+T2W4ETgQeB7YSdLlkj5N6vQC7JVH2OeTOvp7FsqZ0oLpEC9GxMy8/ctch1q0pA0aS6A8RNKj+TMeypqfsWQ34IWIeDa/v46GdEmAyY1V2EmOZmZmVm864xzscu2dyFh+bkTE3yQNICUkngmcQAp0mQgcFxFz80N/BzdS70qqJSyuVYeaat7KqZSSepPmwg+OiBcljaW2JMhyTbUF4CRHMzMzqw+dcQS7oxMZh+Y5yz1I01EelLQF0CMibgYuIk1bIddziaReNZZdtLhUjqRBNAS3AHxM0n55+yTgwWaWXa6WNqiUQFnqTL+ulMo4onB+8ef0DNBX0sfz+1NoSJc0MzMz61Y6XQc7It4ASomMl5Yde4rUwb1TKWnwLmCbJoo8Dzg7T3HYroYqzCYlLy4iJR/ekq+bnqel/BIojaB/E3iU1IF9uoayi24G+khaCJwDPFs49kyu8yLgI8CVzSy7XC1tcA3wJ1IC5VzSUon/R0qiXECKP59dOH8icFVuEwGnk5Ie55NSHa9qYZ3NzMzMuiQnORZIOhgYHRFHd2Ad+gK35ocKuxUnOZqZmVlXISc5mpmZmZm1j245gq12TGSUdDppikbRzIg4u5nl3MKa87ShGcmLXcH62+wS25z2wzX2OcHRzMzMOqNqI9hdYRWRVrcuiYwtuNe1NKybXZNK00TKkxfzOfsX3g8GTo2Ir+YVTQZHxDmSzgTejYhfqAXx5mokYl7SdNK0msfK9lesQ3Pva2ZmZtbVdMsOdp3oS1ph5AaA3MFdawJzRBQfNhxFM+PNW0NZHczMzMzqmudgtxNJ4ySdXXg/VtIFki4tRLCPrHBd3xxm80R+lUatxwHDclT5+Uox77dWuH6spNEV4s2PkvSbwnlH5Gko1T7DeEkLJd0jacvCoVNymQskDW2sDo2U6SRHMzMzqyvuYLefyaSAmpITgNdIU1UGkJIUL5VUvuzga8ARETGItC73ZXn/GGBGRAyMiPFN3bxCvPnvgX6FjvLpwM+rFLER8FhE7Ela4/pbhWMb5jLPaqKMSvVykqOZmZnVFU8RaScRMUfSVpK2BbYE/kbqXN+YI9VflXQ/MASYV7i0FzBB0kBSOuOurVSfkHQ98AVJ1wL7AadWuWQ1DZHnvwR+XTh2Yy7zAUkflrTZutTJSY5mZmZWD9zBbl9TSGmIHyV1VstXBankfOBV0ih3D2BZK9bnWuB3ucwpEbGyGddGI9uV3puZmZl1G54i0r4mkyLLR5A62zOAkZJ65qkaBwGzyq7ZFFgSEatJEeQ98/5qkfKNWeOavJrIK6R0zKZWOulBQ1R6eXz7SABJBwJvRoQnU5uZmVm35RHsdhQRCyVtArwcEUvyQ4X7AXNJo75fj4g/5yX4Sq4AbpZ0KnA78E7ePw9YlWPNJwJzaqjCRFK8+XvAfhHxHjAJ2DIiFjVx7TvAUEkXkeaFFx/IXCZpDmk6yxdrqIeZmZlZ3eqWQTPWQNIEYE5E/Kyj6+KodDMzM+sqHDRjFUl6nDQy/a8dXRczMzOzetEmHey8isRJEXFFG5VfMVWwo0laTEovfL2d7tfshMSy5MVXST+n5YXjjwLrl112Sk6/LJ1zMPB+RDzUguqvZf7Lb9J3zLQ19jkq3czMzLqathrB3oy0JvIaHWxJ6zVzpYpOobPWu6UJiRHx2Qr79q3h0oOBpUDNHezO2oZmZmZmra2tVhEZB+yc0/1m5yTCqcBTAJK+IGlWPn61pJ55/1JJ35U0V9IjkrbO+3eU9HBOO/xOtRvnRMMHJE2T9IykqyT1yCt1TCykJp6fzz8j13GupJslbZj3T8zXPgp8r5F7bS7pzpxueA2gwrF/yfdaIOlred8Fkr6at8dLujdvHyppUrU2aOT+HyQkSpou6ZLcrs9KGpb3byDpV5IW5YcqNyhcv1jSFnn7VEnz8n2vz/v+XtKjkuZIulvS1vkBzDOB8/PPb5hS2uS9+fp7JH2sGW3oJEczMzOrK23VwR4DPJfT/S4ABgHnRcSuknYnrUBxQD6+Cjg5X7cR8EhEDAAeAM7I+38EXBkR/YElNdx/KHAusAewM/APpFCX7SJir1xOaVm6X0fEkHzPRcA/FcrZHtg/Iv6lkft8C3gwpxveApQ6lvuQkhH3BT4JnCHpE6Rl+YblawcDG0vqlfc90EQb1GK9iBgKfI2GpMWvkKaR7J737VN+kaQ9SUv1HZrve14+9CDwyYj4BPAr0ioni4GrgPE5RXIGcDlwXUTsTVqV5LJC8VXb0EmOZmZmVm/aax3sWRHxQt4+jNTJmy3pyfx+p3zsfeDWvP040DdvH0BOCwSur/F+z+eExBuBA4HngZ0kXS7p08Bb+dy98gj7fFJHf89COVNyGY05iJRqSERMI6Uzku93S0S8ExFLSamHw/Jn2kfSh4HlwMOkjvYwUue7WhvUopSuWLyuWMd5rJkSWXIo6bO+ns/7a96/PXBHbpsLWLNtivYDbsjb15M+f0lTbWhmZmZWV9prFZF3CtsijXZ+o8J5K6Jh3cBVrFm/5qwnuFayYET8TdIAYDhpisMJpDWbJwLHRcRcSaNI84sr1bvFImKFpBeAUaT5y/OAQ4CPk0bPoXobNKX0sGJzr2vM5cAPImJqfrBx7DqUUXMbOirdzMzM6kFbjWBXSxm8BxghaSsASX0k7dBEeTNJCYjQMJ2kmqF53nYP0nSUB/Nc4x4RcTNpOsSgfO4mwJI8VaOWsoseIKUaIukzwEfy/hnAcZI2lLQRcDwNI9QzgNH52hmkzv6cQqe6tRXruBewd4Vz7gU+L2nzfF6fvH9T4OW8fVrh/PKf70Os+fOZgZmZmVk31SYd7Ih4A5gpaQFwadmxp0gd3DslzQPuArZposjzgLPzVIXtaqjCbGACaVT4BdL86O2A6Xlayi+B0gj6N4FHSZ34p2sou+jbwEGSFpLmef8JICKeII2Mz8plXxMRpaTFGaTP+3BEvAoso207pFeS5novAv6dNH1kDRGxEPgucL9SMuQP8qGxwBSl9bKLSw/+Dji+9JAjab776fnneQoNc7jNzMzMup26S3LMUxlGR8TRHV0Xax4nOZqZmVlXoSpJju31kKOZmZmZWbfQZTvYkvrnKQrF16MRMb21R68lnV7hXj9u5Xv0zVNqKh37t3zPp/La1U/mfYMlXZbPGSVpQt4+U9Kphf3brmOdljbj3OmS1votrljHpsx/2etgm5mZWdfXXquItLoc3T2wne51LQ3rZre7iPgu8N1Gpr+sNaeiLOFxFLAAeKUt69iYHMnueR9mZmbWbXTZEeyuQNI4SWcX3o9VSnO8VA2JkiMrXNc3r839RH7tnw+NA4blEezzlVIrb61w/VhJoyWNIK2zPSlfc5Sk3xTOO0Ip3bGpz7GFUpLmUfn9hbnucyWNK5z6ea2dJFmxjoWyneRoZmZmdcUd7LY1mbTedskJwGukkfcBwOHApZLKV1F5DTgiIgaRlhksTbEYA8zICYrjm7p5RNxEGj0+Oadm/h7oJ2nLfMrpwM+rlaEU1T4NuDgipuXlCI8F9s2pj8UI9EpJkk3V0UmOZmZmVlfcwW5DeWm+rSRtm0Nu/kbqXN8YEavyMn33A0PKLu0F/DQvSziFFPneGvUJUtLiFyRtRkpgvK3KJb1I65Z/PSLuyvsOB66NiHdzmX8tnF8pSdLMzMysW+myc7C7kCnACOCjpBHtHWu45nzgVdIodw/SWtmt5VrSOtbLSDHmK6ucu5LUWR5O+kWgKS1Kkuy/3doj2CtWrOCll15i2bLWbAJrTO/evdl+++3p1atXR1fFzMysy3IHu+1NBn4KbAF8ijRq/M+SrgP6AAcBFwC9C9dsCrwUEaslnQb0zPurJWQ2Zo1rIuIVSa+Qwn4Ob+LaIMXJT5F0YURcQgoGuljSpIh4V1KfslHsVvXSSy+xySab0LdvXyS11W0MiAjeeOMNXnrpJXbcsZbfA83MzKwSTxFpYzklcRPg5YhYQkqVnAfMJUWUfz0i/lx22b+dVq8AAA1vSURBVBXAaTlVsR/wTt4/D1iVHy48v8YqTASuyg85bpD3TQJejIhFNdR/FXAicKiksyLidmAq8FhOxRxdYz3WybJly9h8883duW4Hkth88839rwVmZmYtVHdJjta0vF72nIj4WUfXpahSkuOiRYvYfffdO6hG3ZPb3MzMrGnVkhw9RaSbkfQ4aUT8Xzu6LmZmZmb1yB3sTkpSX+DWiNiriXP2j4gb8vvBwKkR8VVJo4DBEXGOpDOBdyPiF8DlwJ0RsbxQzqPA+mXFn5LDfIr3WxoRG9dY/4m5/jdJugb4QUQ8Ve2aWpIc+46ZVsvta7Z43FFNnrP//vvz0EMPtep9q1m8eDEPPfQQJ510Urvd08zMzFqPO9hdW1/gJOAGaDw1salkx4jYty0rGRFfasvy21p7dq5XrlzJ4sWLueGGG9zBNjMz66L8kGM76obJjqXzp+fR9S5p443ToP306dP51Kc+xbHHHstOO+3EmDFjmDRpEkOHDqV///4899xzAIwaNYozzzyTwYMHs+uuu3LrrelHsmzZMk4//XT69+/PJz7xCe677z4AJk6cyDHHHMOhhx7KYYcdxpgxY5gxYwYDBw5k/PjxLF68mGHDhjFo0CAGDRr0QYd/+vTpHHzwwYwYMYJ+/fpx8sknU3qmYvbs2ey///4MGDCAoUOH8vbbb7Nq1SouuOAChgwZwt57783VV1/d3k1pZmbWLXgEu31NBn4I/Di/PwG4BDiStOb1FsBsSQ+UXVdKdlwmaRfgRlJHeQwwOiKOhhRLXu3mebrGOfmax5SW5vhvSVtGxF+oPdlxKnBRRNxVluz4rqQ+NbVEQ3lfBr4M0PPDWzZxdsebO3cuixYtok+fPuy000586UtfYtasWfzoRz/i8ssv54c//CGQpnnMmjWL5557jkMOOYQ//vGP/PjHP0YS8+fP5+mnn+bII4/k2WefBeCJJ55g3rx59OnTh+nTp/P973//g475u+++y1133UXv3r35wx/+wIknnkjpYdA5c+awcOFCtt12Ww444ABmzpzJ0KFDGTlyJJMnT2bIkCG89dZbbLDBBvzsZz9j0003Zfbs2SxfvpwDDjiAI4880kvymZmZtTJ3sNtRRMyRtJWkbYEtKUt2BF6VVEp2nFe4tBcwQdJAUojLrq1Un5BUSna8lrRG96lVLiklO54dEaXgmWrJjrXU4SfATwDW32aXTr+kzZAhQ9hmm5Rsv/POO3PkkUcC0L9//w9GpAFOOOEEevTowS677MJOO+3E008/zYMPPsi5554LQL9+/dhhhx0+6GAfccQR9OlT+XeTFStWcM455/Dkk0/Ss2fPD64BGDp0KNtvvz0AAwcOZPHixWy66aZss802DBmSAkI//OEPA3DnnXcyb948brrpJgDefPNN/vCHP7iDbWZm1srcwW5/3SnZsVkqJTl2Nuuv3/AsaI8ePT5436NHD1aubGi68nW7m1rHe6ONNmr02Pjx49l6662ZO3cuq1evpnfvhkyiYn169uy5Rh3KRQSXX345w4cPr1oXMzMzaxnPwW5/k4F/JHWypwAzgJGSekrakpTsOKvsmk2BJRGxGjiFVk52JD3weBGps11NKdmxn6QL8767gNMlbQjQ3Cki9WrKlCmsXr2a5557jueff57ddtuNYcOGMWnSJACeffZZ/vSnP7Hbbrutde0mm2zC22+//cH7N998k2222YYePXpw/fXXs2rVqqr33m233ViyZAmzZ88G4O2332blypUMHz6cK6+8khUrVnxQh3feeadaUWZmZrYOPILdziJioaQPkh3zQ4X7kZIdg5zsmJfgK7kCuFnSqcDtVEh2JCU2zqmhChNJyY7vAftFxHukZMcta012lHQiMFXS2xFxRZ668pik94HfA/+vhnqsk1qW1esMPvaxjzF06FDeeustrrrqKnr37s1ZZ53FV77yFfr37896663HxIkT1xiBLtl7773p2bMnAwYMYNSoUZx11ll87nOf4xe/+AWf/vSnq452A3zoQx9i8uTJnHvuubz33ntssMEG3H333XzpS19i8eLFDBo0iIhgyy235De/+U3VsszMzKz5nORonSbZsV6SHEeNGsXRRx/NiBEjOroq66QrtrmZmVl7c5KjNcrJjmZmZmaty3OwO4G8zvWCGs45qfB+sKTL8vaoPAqNpDPzVJLS/m2rlRsR+0TEQeXJjpJW5bWyS6/++VjFNa1La203UveaklpqSXLsCiZOnNhlR6/NzMys5TyC3XX0pRVSG2sREfvmWPSB61rZsvL2b/qsqtc3uQqHtQ5PGTMzM2s5j2C3kXpIbZQ0XtJCSffkFU5KTsllLpA0tLB/jzzC/bykrxbKWdpUezWmd+/evPHGG+74tYOI4I033lhjGUAzMzNrPo9gt52untq4EfBYRJwv6WLgW8A5+diGETFQ0kG5jL3y/n7AIaRlAJ+RdGVErKhWz6aSHLfffnteeukl/vKXv1QrxlpJ7969PwiuMTMzs3XjDnYbqYPUxtWkXxIAfgn8unDsxlzmA5I+LGmzvH9ansu9XNJrwNbAS03Uq2qSY69evZw0aGZmZl2KO9htqyunNpaLRraL75cX9q2imd+vrpDkaGZmZtYUz8FuW105tbFHrjekhysfLBwbCSDpQODNiKiP5T/MzMzMWoFHsNtQF09tfAcYKuki0rzw4gOZyyTNIU1n+WIN9TAzMzPrNpzk2M10ltTGSiS9DTzT0fXoJLYAXu/oSnQSbos1uT0auC0auC0auC3W5PZo0NptsUNErL1CA+5gdyuF1MYjisEynYWkxxqLHO1u3BYN3BZrcns0cFs0cFs0cFusye3RoD3bwlNEupGI2Kd8n6RHgfXLdp8SEfPbp1ZmZmZm9cUd7G4uIvbt6DqYmZmZ1ROvImKdyU86ugKdiNuigdtiTW6PBm6LBm6LBm6LNbk9GrRbW3gOtpmZmZlZK/IItpmZmZlZK3IH28zMzMysFbmDbW1C0qclPSPpj5LGVDi+vqTJ+fijxbAdSd/I+5+RNLzWMjuzNmqPxZLmS3pS0mPt80labl3bQtLmku6TtDSv5168Zp/cFn+UdJkktc+naZk2aovpucwn82ur9vk0LdOCtjhC0uP55/+4pEML13S370W1tuiS3wtoUXsMLXzeuZKOr7XMzqqN2qJb/V1SOP6x/P/Q0bWW2SwR4Zdfrfoixbs/B+wEfIiUXLlH2TlnAVfl7X8EJuftPfL56wM75nJ61lJmZ321RXvkY4uBLTr687VjW2wEHAicCUwou2YW8ElAwG3AZzr6s3ZgW0wHBnf052vHtvgEsG3e3ouUnNtdvxfV2qLLfS9aoT02BNbL29uQUonXq6XMzvhqi7bI7xfTjf4uKRy/CZgCjK61zOa8PIJtbWEo8MeIeD4i3gd+BRxbds6xwHV5+ybgsDy6dCzwq4hYHhEvAH/M5dVSZmfVFu3RVa1zW0TEOxHxILCseLKkbYAPR8Qjkf4v+QvguDb9FK2j1duiC2tJW8yJiFfy/oXABnnkqjt+Lyq2RbvUuu20pD3ejYiVeX9voLSqQ1f9+6Qt2qKrasnfq0g6DniB9N9Jc8qsmTvY1ha2A14svH8p76t4Tv6P/k1g8yrX1lJmZ9UW7QHpf5B35n8K/nIb1LsttKQtqpX5UhNldkZt0RYl1+Z/7v1mF5kW0Vpt8TngiUhJtd39e1Fsi5Ku9r2AFraHpH0lLQTmA2fm413175O2aAvoZn+XSNoYuBD49jqUWTMHzZh1XQdGxMt5LuVdkp6OiAc6ulLW4U7O34tNgJuBU0ijt3VN0p7AJcCRHV2XjtZIW3TL70VEPArsKWl34DpJt3V0nTpKpbaIiGV0v79LxgLjI2JpW/6e6RFsawsvA39XeL993lfxHEnrAZsCb1S5tpYyO6u2aA8iovTna8AtdI2pIy1pi2plbt9EmZ1RW7RF8XvxNnAD3eB7IWl70n8Dp0bEc4Xzu933opG26KrfC2il/04iYhGwlDw3vYYyO6O2aIvu+HfJvsD3JC0Gvgb8P0nn1FhmzdzBtrYwG9hF0o6SPkR6uGBq2TlTgdPy9gjg3jxPcirwj3kO5Y7ALqQHlWops7Nq9faQtFEeiULSRqSRqgXt8FlaqiVtUVFELAHekvTJ/M/epwK/bf2qt7pWbwtJ60naIm/3Ao6mzr8XkjYDpgFjImJm6eTu+L1orC268PcCWtYeO+aOFZJ2APqRHujrqn+ftHpbdMe/SyJiWET0jYi+wA+B/4yICTWWWbt1fTrSL7+qvYDPAs+Snsj9t7zv34Fj8nZv0tO7fyR1oHcqXPtv+bpnKDz1X6nMrvJq7fYgPeU8N78WdqX2aGFbLAb+Shp9eYn8hDcwmPSXwnPABHJKbWd/tXZbkFYXeRyYl78XPyKvOtPZX+vaFsBFwDvAk4XXVt3xe9FYW3Tl70UL2+OU/HmfBJ4AjqtWZld4tXZb0E3/LimUMZa8ikhrfy8clW5mZmZm1oo8RcTMzMzMrBW5g21mZmZm1orcwTYzMzMza0XuYJuZmZmZtSJ3sM3MzMzMWpE72GZmZmZmrcgdbDMzMzOzVvT/ATjuGljpN+HnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importance.set_index(\"feat\").sort_values(ascending=True, by=\"importance\").plot.barh(figsize=(10, 15))\n",
    "plt.title(\"TA Feature Importance for Predicting BTC returns\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrain on top features\n",
    "\n",
    "Improved corrrelation by 5 percentage points!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_feats = feat_importance.set_index(\"feat\").importance.sort_values(ascending=False).head(20).index.values\n",
    "#regressor_top = RandomForestRegressor()\n",
    "regressor_top = LinearRegression()\n",
    "regressor_top.fit(X_full_train[top_feats], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129e1e890>"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of sample\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "pdf = pd.DataFrame(dict(pred=regressor_top.predict(X_full_test[top_feats]), actual=y_test))\n",
    "pdf['pos_pred'] = pdf.pred.gt(0)\n",
    "pdf['pos_actual'] = pdf.actual.gt(0)\n",
    "\n",
    "pdf.plot.scatter(0, 1)\n",
    "#plt.xlim(-0.1, 0.1)\n",
    "#plt.ylim(-0.10, 0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pred</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>0.217987</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pred    actual\n",
       "pred    1.000000  0.217987\n",
       "actual  0.217987  1.000000"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf[[\"pred\", \"actual\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06142693380160941"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(pdf.actual.subtract(pdf.pred).pow(2).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall accuracy\n",
    "(pdf['pos_pred'] == pdf['pos_actual']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf['pos_actual'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 actual   R-squared:                       0.111\n",
      "Model:                            OLS   Adj. R-squared:                  0.102\n",
      "Method:                 Least Squares   F-statistic:                     12.22\n",
      "Date:                Mon, 24 Aug 2020   Prob (F-statistic):           0.000713\n",
      "Time:                        15:53:08   Log-Likelihood:                 146.11\n",
      "No. Observations:                 100   AIC:                            -288.2\n",
      "Df Residuals:                      98   BIC:                            -283.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0118      0.007     -1.680      0.096      -0.026       0.002\n",
      "pred           0.1393      0.040      3.495      0.001       0.060       0.218\n",
      "==============================================================================\n",
      "Omnibus:                      102.398   Durbin-Watson:                   2.133\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1986.483\n",
      "Skew:                          -3.128   Prob(JB):                         0.00\n",
      "Kurtosis:                      23.919   Cond. No.                         7.11\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.2)"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import statsmodels.regression.linear_model as sm\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "\n",
    "comb = pdf[[\"pred\", \"actual\"]].copy()\n",
    "\n",
    "comb = comb.dropna()\n",
    "X = pdf[[\"pred\"]]\n",
    "Y = pdf[[\"actual\"]]\n",
    "model2 = sm.OLS(Y,sm.add_constant(X), data=comb)\n",
    "model_fit = model2.fit()\n",
    "print(model_fit.summary())\n",
    "\n",
    "#Plot\n",
    "pdf[[\"pred\", \"actual\"]].plot(kind='scatter', x=\"pred\", y=\"actual\")\n",
    "#plt.ylim(-0.2, 0.2)\n",
    "#plt.xlim(-0.2, 0.2)\n",
    "#Seaborn\n",
    "sns.lmplot(x=\"pred\", y=\"actual\", data=pdf)\n",
    "plt.ylim(-0.1, 0.2)\n",
    "#plt.xlim(-0.2, 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add lag features\n",
    "\n",
    "Looks like adding lag features made it worse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_crypto_data(\"BTC/USDT\", \"2019-01-01\", \"2020-08-23\").reset_index().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = add_all_ta_features(\n",
    "    df, open=\"open\", high=\"high\", low=\"low\", close=\"close\", volume=\"volume\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.iloc[: -1]\n",
    "X[\"pct_change\"] = df.close.pct_change()\n",
    "X[\"pct_change_lag1\"] = X[\"pct_change\"].shift()\n",
    "X = X.fillna(-1)\n",
    "y = df.close.pct_change().shift(-1).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"dt\" in X.columns:\n",
    "    del X[\"dt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_1 = X.shift().fillna(-1)\n",
    "X_1.columns = [c + \"_1\" for c in X.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_comb = pd.concat([X, X_1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_full_train, X_full_test, y_train, y_test = X_comb.iloc[:-100], X_comb.iloc[-100:], y.iloc[:-100], y.iloc[-100:]\n",
    "#X_filtered_train, X_filtered_test = X_full_train[X_filtered.columns], X_full_test[X_filtered.columns]\n",
    "X_filtered_train, X_filtered_test = X_full_train, X_full_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_full = LinearRegression()\n",
    "#regressor_full = RandomForestRegressor()\n",
    "regressor_full.fit(X_full_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.15, 0.2)"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of sample\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "pdf = pd.DataFrame(dict(pred=regressor_full.predict(X_full_test), actual=y_test))\n",
    "\n",
    "pdf.plot.scatter(0, 1)\n",
    "plt.xlim(-0.1, 0.2)\n",
    "plt.ylim(-0.15, 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pred</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.332945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>0.332945</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pred    actual\n",
       "pred    1.000000  0.332945\n",
       "actual  0.332945  1.000000"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearRegression' object has no attribute 'feature_importances_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-418-fe84e39d411f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeat_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"importance\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mregressor_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"feat\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX_full_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'LinearRegression' object has no attribute 'feature_importances_'"
     ]
    }
   ],
   "source": [
    "feat_importance = pd.DataFrame({\"importance\": regressor_full.feature_importances_, \"feat\": X_full_train.columns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 2)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TA Feature Importance for Predicting BTC returns')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importance.set_index(\"feat\").sort_values(ascending=True, by=\"importance\").plot.barh(figsize=(15, 30))\n",
    "plt.title(\"TA Feature Importance for Predicting BTC returns\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_feats = feat_importance.set_index(\"feat\").importance.sort_values(ascending=False).head(50).index.values\n",
    "regressor_top = RandomForestRegressor()\n",
    "regressor_top.fit(X_full_train[top_feats], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.15, 0.15)"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of sample\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "pdf = pd.DataFrame(dict(pred=regressor_top.predict(X_full_test[top_feats]), actual=y_test))\n",
    "\n",
    "pdf.plot.scatter(0, 1)\n",
    "plt.xlim(-0.1, 0.1)\n",
    "plt.ylim(-0.15, 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pred</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.04023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>0.04023</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred   actual\n",
       "pred    1.00000  0.04023\n",
       "actual  0.04023  1.00000"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking intro specifiying as classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12648c3d0>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    499.000000\n",
       "mean       0.002803\n",
       "std        0.041368\n",
       "min       -0.395048\n",
       "25%       -0.013306\n",
       "50%        0.001246\n",
       "75%        0.018538\n",
       "max        0.171968\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One approach would be to specify as a ternary classification problem\n",
    "#\n",
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
